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Popkave, Kyle Marissa.
The relationship between parent identified sleep problems, internalizing behaviors, externalizing behaviors, and adaptive functioning in a pediatric population
h [electronic resource] /
by Kyle Marissa Popkave.
[Tampa, Fla.] :
b University of South Florida,
ABSTRACT: Pediatric sleep problems are among the most common pediatric health issues faced by families today. Sleep problems can have a deleterious impact on children's academics, behaviors, social-emotional development, health, and/or safety. Once sleep problems are identified and treated, many of the associated negative impacts can be ameliorated. The purpose of the current study was to examine prevalence rates of symptoms of sleep disorders in young children, and the relationship between these symptoms and various behavior problems. One hundred and four children, ages 2 to 5 years, attending a pediatric health clinic served as the participants in this study. Data on sleep disorder symptoms were derived from the Sleep Disorders Inventory for Students, Children's Form. The Child Behavior Checklist was used to measure internalizing and externalizing behaviors, and adaptive behavior was assessed through ratings on the Adaptive Behavior Assessment System, Second Edition. Results indicated that a total of 31% of the sampled children were at high risk for at least one type of sleep disorder. Children rated as high risk for having a sleep disorder displayed more externalizing and internalizing problems, as compared to children whose sleep was reported to be in the normal range. No significant differences were found between adaptive behavior scores and risk for having a sleep disorder. The implications of these results for school psychologists and directions for future practice and research are discussed.
Thesis (Ed.S.)--University of South Florida, 2007.
Includes bibliographical references.
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Advisor: Kathy Bradley-Klug, Ph.D.
x Counselor Education
t USF Electronic Theses and Dissertations.
The Relationship between Pare nt Identified Sleep Problems, Internalizing Behaviors, Externalizing Behaviors, and Adaptive Functioning in a Pediatric Population by Kyle Marissa Popkave A thesis submitted in partial fulfillment of the requirements for the degree of Educational Specialist Department of Psychological and Social Foundations College of Education University of South Florida Co-Major Professor: Kathy Bradley-Klug, Ph.D. Co-Major Professor: Kathleen Armstrong, Ph.D. John Ferron, Ed.D. Date of Approval: May 21, 2007 Keywords: Health, Early Interven tion, Child, Pre-School, Disorders Copyright 2007, Kyle Marissa Popkave
i Table of Contents List of Tables iii List of Figures iv Abstract v Chapter 1: Introduction 1 Statement of the Problem 1 Understanding Sleep 3 Normal Sleep 3 Developmental Differences in Sleep 3 Measuring Sleep 4 Five Major Pediatric Sleep Disorders Negativ ely Impacting Children 4 The Relationship Between Sleep Diso rders, Externalizing Behaviors, Internalizing Behaviors, and Adaptive Functioning 7 Purpose of the Study 8 Research Questions 9 Significance of this Study 10 Organization of Remaining Chapters 10 Chapter 2: Review of the Literature 11 Overview 11 Introduction 11 Normal Sleep 13 The Relationship Between Sleep and Development 16 Measures to Assess Sleep Disorders 18 Pediatric Sleep Disorders 19 Periodic Limb Movement Disorder 19 Restless Leg Syndrome 20 Delayed Sleep Phase Syndrome 21 Narcolepsy 21 Obstructive Sleep Apnea 22 Deleterious Effects of Sleep Disorders on Young Children 24 Defining Internalizing and Externaliz ing Disorders 25 Developmental Differences in Psychopathology 25 Internalizing Disorders and Sleep Problems 28 Externalizing Disorders and Sleep Problems 32 Adaptive Behavior in Young Children 37
ii Summary 41 Chapter 3: Methods 42 Introduction 42 Participant Characteristics 42 Setting 43 Instrumentation 44 Sleep Disorder Inventory for Students 44 Child Behavior Checklist 46 Adaptive Behavior Assessment System Â– Second Edition 47 Procedure 49 Data Analysis 50 Chapter 4: Results 52 Instrumentation Reliability 52 Sleep Disorders Inventory for Students 52 Child Behavior Checklist 52 Adaptive Behavior Assessment System Â– Second Edition 52 Descriptive Statistics 52 Prevalence of Sleep Disorders 53 Externalizing Problems and Sleep Disorders 54 Internalizing Problems and Sleep Disorders 57 Adaptive Skills and Sleep Disorders 61 Relationship between SDIS-C and CBCL 63 Summary 64 Chapter 5: Discussion 66 Research Question 1 67 Research Question 2 71 Research Question 3 73 Research Question 4 75 Research Question 5 77 Implications for School Psychologists: School -Based Health Services 78 Limitations and Implications for Future Resear ch 81 Conclusion 83 References 85
iii List of Tables Table 1 Participant Characteristics 43 Table 2 SDIS-C Descriptive Statistics 58 Table 3 CBCL Descriptive Statistics 58 Table 4 ABAS-II Descriptive Statistics 58 Table 5 Prevalence of Sleep Disorders as Measured by the SDIS-C 59 Table 6 SDIS-C Subscale Percentages 59 Table 7 Externalizing Problems Means a nd Standard Deviations 59 by Sleep Score Table 8 Internalizing Problems Means and St andard Deviations 60 by Sleep Score Table 9 Adaptive Behavior Functioning Means a nd Standard Deviations 63 by Sleep Score
iv List of Figures Figure 1 Distribution of Externaliz ing Behavior Scores by Sleep Disorder 55 Risk Level Figure 2 Distribution of Internaliz ing Behavior Scores by Sleep Diso rder 57 Risk Level Figure 3 Distribution of Adaptive Beha vior Scores by Sleep Disorder 62 Risk Level Figure 4 Scatterplot of Sleep Scores as Measured by the CBCL sleep score 64 and the SDIS-C sleep index score
v The Relationship between Pare nt Identified Sleep Problems, Internalizing Behaviors, Externalizing Behaviors, and Adaptive Functioning in a Pediatric Population Kyle Marissa Popkave ABSTRACT Pediatric sleep problems are among the most common pediatric health issues faced by families today. Sleep problems can have a deleterious impact on childrenÂ’s academics, behaviors, social-emotional develo pment, health, and/or safety. Once sleep problems are identified and treated, many of the associated negative impacts can be ameliorated. The purpose of the current study was to examine prevalence rates of symptoms of sleep disorders in young ch ildren, and the relati onship between these symptoms and various behavior problems. One hundred and four children, ages 2 to 5 years, attending a pediatric health clinic served as the participants in this study. Data on sleep di sorder symptoms were derived from the Sleep Disord ers Inventory for Students, Ch ildrenÂ’s Form. The Child Behavior Checklist was used to measure inte rnalizing and externa lizing behaviors, and adaptive behavior was assessed through rati ngs on the Adaptive Behavior Assessment System, Second Edition. Results indicated that a total of 31% of the sampled children were at high risk for at leas t one type of sleep disorder. Ch ildren rated as high risk for having a sleep disorder displa yed more externalizing and internalizing problems, as compared to children whose sleep was reported to be in the normal range. No significant differences were found between adaptive beha vior scores and risk for having a sleep
vi disorder. The implications of these results for school psychologists and directions for future practice and research are discussed.
1 CHAPTER 1 Introduction Statement of the problem Research on sleep has revealed the nega tive impact sleep deprivation can have on psychological and physical health. Sleep imp acts the daily functioning of people at all ages. Research conducted by the National Cent er of Sleep Disorders Research (2003) estimates that an upwards of 70 million Americans are negatively impacted by sleep related problems. Of these estimates, approximately 15% of the 70 million Americans with sleep problems are children (Luginbuehl 2004). Only recently has pediatric sleep medicine begun to be studied in childre n. There is limited epidemiologic research focusing on sleep in infants and young children (Owens, 2005). Compared to adults, little is known about the magnitude and distributi on, causes, consequences, and assessment of sleep loss and sleepiness in young children (National Sleep Disorder Research Plan, 2005). Because little is known about pediatric sl eep disorders and how to diagnose them, it is estimated that only 1-to-2% of children with sleep disorders are being diagnosed and treated. Of the children who ar e diagnosed as having a sleep disorder, 12 Â– 15% of them have learning, behavior, and/or emotional re gulation challenges (Na tional Sleep Disorder Research Plan, 2005). In addition, many child ren are diagnosed as having a learning disability, when in fact they actually have a correctable sleep disorder (Gozal, 1998; Marcotte et al., 1998). Therefore, it is vital th at sleep disorders are identified and treated
2 at the earliest possible age in order to prevent the negative academic, behavioral, emotional, and health outcomes associated with them. Further research is needed examining the relationship between sleep disorders and emotional, and behavioral outcomes (Halborow & Marcus, 2003). Spec ifically, research is needed to identify which age groups are at highest risk for specifi c sleep disorders, and when intervention implementation would have the highest impact in terms of improving overall functioning. Research is also needed to study the prevalence rates in young children as well as the effects that sleep disorders have on emo tional and behavioral outcomes (Wiggs & Stores, 1996). Current research suggests a link between sleep disorders and behavioral outcomes, but the age at which children with sleep disorders begin experiencing behavioral difficulties is still unknown. One possible reason that sleep disorder s and their impact on young children are unknown is because only a handful of program s train child service providers on sleep disorders and their effects on everyday func tioning (Wiggs & Stores, 1996). Children with sleep disorders usually do not complain of sleep problems. (Kryger, 2005). Sleep disorders in children are typically suspected by adults, but because little knowledge exists regarding sleep disorders, many children go unidentified. Professionals do not know what to look for, and many times, if a sleep disorder is suspected, professionals do not know where to refer these children (Benbadis, 1998). Even if a child is suspected of having a sleep disorder, professi onals have to rely on the validity of parental concerns and opinions regarding their ch ildÂ’s sleep (Owens, 2005). It is important to enhance the current research on sleep diso rders, as well as disseminate this information to child
3 service providers so that children do not go undiagnosed, a nd early intervention can be implemented. Understanding sleep In order to understand sleep disorders, it is helpful to understand normal sleep. What constitutes normal sleep differs by age; therefore developmental differences in sleep will be briefly discussed. Finally, the in struments used to aid in the diagnosis of sleep disorders will be discussed. Normal sleep There are 5 well-defined, ordere d stages of normal sleep. The stages of sleep are made up of rapid ey e movement sleep (REM), and non-rapid eye movement sleep (NREM) (Morrison, 2004). NREM sleep makes up 4 of the 5 stages of normal sleep. Stage 1 of NREM is when a person transitions from wake fulness into sleep; it is known as light sleep. Stage 2 of NREM is considered to be the first stage of actual sleep, but a person can be woken rather easily during this stage. Stages 3 and 4 of NREM are deep, slow wave sleep, and during these st ages, it is difficult to wake the person. The last stage of sleep is REM sleep. During REM sleep, the body is rather active where eyes dart back and forth; heart rate and blood pre ssure increase; breathing is irregular, shallow, and fast; the brain is acti ve; and dreams occur (Anders, Sadeh & Appareddy, 1995). It takes approximately 90 Â– 110 minutes to go th rough stages 1 Â– 5 of sleep. Once all 5 stages of sleep are complete, stage 1 of sl eep starts all over ag ain. People typically go through stages 1 Â– 5 of sleep approximately 4 Â– 6 times a night. Developmental diffe rences in sleep The way people sleep varies across ages. For instance, infants fall directly into REM sleep and spend more than half of their time sleeping in this stage. In cont rast, adults spend almost one-hal f of their sleep in stages 1
4 and 2 of NREM sleep, and do not reach REM sleep until after stage 4 of NREM. As children grow older, the amount of REM sleep decreases a nd the amount of NREM sleep increases. Also, younger children need more sleep per day than older ch ildren and adults. Measuring sleep Subjective reports of sleepiness can be unreliable (Thorpy et al., 2006) so it is important to quantify sleep using reliable methods. Polysomnography and multiple sleep latency test (MSLT) are the tw o most commonly used methods to measure sleep (Davey, 2005). Polysomnograms are overn ight tests (also referred to as sleep studies) that extensively measure sleep archit ecture, including time in bed and total sleep time; sleep efficiency and latency; REM latency; the percentage of time spent in each sleep stage; arousals; and sleep frag mentation. MSLT is usually follows a polysomnography, and measures the average time it takes to fall asleep. Five major pediatric sleep disord ers negatively impacting children There are five sleep disorders that ar e most commonly seen in children that negatively impact academic performance, beha vior, social-emotional functioning, and/or health (Coccagna, 1990; Hla, 1994; Wise, 1998). There are a nu mber of other pediatric sleep disorders, but these disorders do not have as much influence on the daily functioning of children. The five sleep di sorders that do negatively impact daily functioning of children are: (1) Periodic Li mb Movement Disorder (PLMD), (2) Restless Legs Syndrome (RLS), (3) Delayed Sleep Phase Syndrome (DSPS), (4) Narcolepsy, and (5) Obstructive Sleep Apnea Syndrome (OSAS). Periodic Limb Movement Disorder (PLMD) is characterized by limb contractions that occur at 15 Â– 40 second in tervals lasting 5 seconds or longer causing the toes, legs, thighs or arms to jerk repetitively. This re petitive jerking movement can wake a child out
5 of sleep and prevent a child from getting sufficient sleep (Luginbuehl, 2004). PLMD is diagnosed when there are more than five periodic limb movements per hour and those movements are associated w ith a sleep disruption. Restless Leg Syndrome (RLS) can occur with or without PLMD, but children with RLS also usually have PLMD (The International Restless Legs Syndrome Study Group, 1995). In RLS, a child will experience uncomfortable sensat ions in his or her legs. These sensations are typically described as tingling, searing or crawling sensations (Luginbuehl, 2004). These uncomfortable sensat ions cause an uncont rollable urge to move ones legs. This urge worsens when people sit or lie down, and the urge to move is totally or partially relieved by movement (Gringas, 2005; Lavigne & Montplaisir, 1994). Because the urge to move worsens when pe ople lie down, these sensations contribute to sleep disruption (The International Restless Legs Syndrome Study Group, 1995). Delayed Sleep Phase Syndrome (DSPS) is most common in adolescence because it is associated with change s in the circadian rhythm during puberty. DSPS is also characterized by poor sleep habits and hygi ene (Carskadon, Viera, & Acebo, 1993). To be diagnosed with DSPS, the inability to fa ll asleep or wake up at normal times must persist for at least six months (Roehr & Roth, 1994). DSPS tends to begin when sleep patterns change, which can de lay the circadian sleep cycl e (Wolfson & Carskadon, 1998). When a child or adolescent has DSPS, they have difficulty falling asleep or staying asleep throughout the night. Children or adol escents with DSPS might stall going to bed or refuse to go to bed at th e appropriate time. DSPS is also associated with children or adolescence not having limits set for them to help them establish good sleep habits (Luginbuehl, 2004).
6 Narcolepsy is another sleep disorder th at is characterized by disrupted nighttime sleep, cataplexy, sleep paralysis, hypongogic hallucinations when falling asleep or awakening, and daytime sleep attacks (Luginbuehl, 2004). A daytime sleep attack is when a person takes frequent short naps (Benbadis, 2005). Cataplexy is provoked by emotions such as laughter, surprise, or ange r and causes a sudden decrease in muscle tone (Guilleminault, Mignot, & Part inen, 1994). With sleep paralysi s, a person is fully aware of their surroundings but cannot move their extremities, speak, or open their eyes. Hypnagogic hallucinations are auditory and visu al disturbances (Guilleminault, Mignot, & Partinen, 1994). Oftentimes, hypnagogic hallucin ations co-occur with sleep paralysis. Excessive daytime sleepiness is associated with narcolepsy. Children typically do not show all of the characterizations of narc olepsy, which makes it difficult to diagnose (Aldrich, 1992). Obstructive Sleep Apnea Syndrome (OSAS) is the most frequently researched sleep disorder in children and is character ized by Â“pauses in breathing (apneas) during sleep and/or hypopnea events that result in a si gnificant decrease of oxygen to the arterial blood flowÂ” (Luginbuehl, 2004). The most common cause of OSAS in children is enlarged tonsils and adenoids that obstruct airway passages (Bower & Buckmiller, 2001). Airway passages can also be obstructed by physical abnormalities of the tongue, palatal size and position, and the jaw. The most co mmon symptoms of OSAS in children are gasping, a pause in breathing, raspy breat hing, choking, snorting, excessive sweating, open mouth breathing during the daytime or nighttime, and snoring (Gaultier, 1992). PLMD, RLS, DSPS, Narcolepsy, and OS AS have significan t negative daytime and nighttime effects. Negative academic, beha vioral, and social-emotional outcomes are
7 associated with sleep disorders. Federal polic ies such as Individuals with Disabilities Education ActÂ—Amendments of 2004 (IDEA ) emphasize the need and importance for early identification and early intervention of disorders effec ting childrenÂ’s daily functioning. The relationship between sleep disorders, in ternalizing behavior, ex ternalizing behavior, and adaptive functioning Early identification of sleep disorders is extremely important because of the suggested negative consequences of sleep disorders on behavior and every day functioning. Sleep deprivati on is related to depressed mood, anxiety, as well as hyperactivity, irritability, or shortened attention span (Aaronen, Paavonen, Fjallberg, & Torronen, 2000; Dahl, 1998; Dollinger, Molina, & Campo, 1996; Reite, 1998; Sadeh, McGuire, Sachs, Seifer, Tremblay, Civita & Hayden, 1994). For instance, Dollinger et al. (1996) found that children suffering from anxiety had more sleep problems than children without anxiety. Another study conduc ted by Aaronen et al. (2000) found that less amount of sleep in children ages 8 Â– 12 years was associated with increases in emotional and behavioral problems. Decreased sleep quality is also highly correlated with depression (Sadeh et al., 1994). Studies have also suggested th at inattentive, hyperactive, and aggressive behaviors are comorbid w ith sleep disordered breathing (Chervin & Archibold, 2001; Guilleminault et al. 1981; Picchietti & Walters, 1999). In a study conducted by Stores and Wiggs (1998), children ages 5-16 years with sleep disturbances exhibited more challenging behaviors such as irritability and hyperactivity as compared to controls. Picchietti et al. (1998) found similar results in a study of children ages 2 Â– 15 years. Specifically, there was a high inciden ce rate of PLMD within this sample of
8 children who were all diagnosed with Atte ntion-Deficit/Hyperactiv ity Disorder (ADHD). Chervin et al. (1997) also l ooked at the relationship between children ages 2 Â– 18 years diagnosed with ADHD and sleep problems. The results of this study showed strong relationship between snoring, sleepiness, a nd restless legs and ADHD, thus providing more support for an overlap between ADHD symp toms and symptoms of sleep disorders. Children diagnosed with ADHD have also been found to have poor daily functioning skills (Stein, Szumowski, Blondis, &Roi zen,1995). Likewise, children experiencing internalizing and externalizing behaviors also have been found to have poorer adaptive skills than children without these problems (Palermo et al., 2002). Even though there is research linking internalizing and externaliz ing behavior problems to adaptive behavior, there is no research on how sleep impact s adaptive functioning in children. Purpose of the study The primary purpose of this study was to assess the prevalence rates of children ages 24 months to 5 years refe rred to a clinic setting displa ying sleep disorder symptoms, using the Sleep Disorders Inventory for St udents Â– ChildrenÂ’s Version (SDIS-C). The SDIS-C is a screening tool that aids in the identification of children at risk for PLMD, RLS, DSPS, Narcolepsy, and OSAS. A secondary purpose of the study was to examine if there was a difference between children w ho fall into high, cautionary or low risk ranges for having a sleep disorder as measur ed by the SDIS-C and their scores on the Child Behavior Checklist (CBCL), indicati ng problems with internalizing and/or externalizing behavior. A third purpose of this study was to examine if there was a difference between children who fall into high, ca utionary or low risk ranges for having a sleep disorder as measured by the SDIS-C and their overall adaptive functioning as
9 measured by Adaptive Behavior Assessment System Â– Second Edition (ABAS-II). A final goal of this study was to assess the rela tionship between sleep pr oblems as measured by the CBCL and the sleep index score as measured by the SDIS-C. By examining these issues, this study c ontributed to the research and literature concerning sleep disorders in young children. Research questions This research study investigated the preval ence rates of risk for sleep disorders in an at-risk population of children ages 2 Â– 5 years as well as examined the relationship between sleep disorders, problem behaviors, and adaptive skills. Therefore, the following research questions were addressed: 1. What is the prevalence rate of children at risk for sleep disorders, as measured by the Sleep Disorders Inventory for Students Childr enÂ’s version (SDIS-C), in children ages 2 Â– 5 years presenting to a university-based child development clinic for assistance? 2. What is the relationship between normal, cau tionary and high-risk range sleep disorder symptoms, as measured by the SDIS-C, a nd externalizing behavior problems, as measured by the Child Behavior Checklist (CBCL)? 3. What is the relationship between normal, cau tionary and high-risk range sleep disorder symptoms, as measured by the SDIS-C, a nd internalizing behavior problems, as measured by the CBCL? 4. What is the relationship between children at risk for sleep disorders in the normal, cautionary, and high-risk range, as measured by the SDIS-C and their functional adaptive skills as measured by the Adaptive Behavior Assessment System Â– Second Edition (ABAS-II)?
10 5. What is the relationship between the sleep problem score on the CBCL and the total sleep index score on the SDIS-C? Significance of this study This study provided valuable informati on regarding the prevalence of sleep disorders in a young pediatric clinical popul ation as well as in formation about the relationship between sleep disorders, behavior al, and adaptive functioning. Because sleep disorders in children are s till under-diagnosed, in formation on the prevalence of sleep disorders in children can potentially increase the awareness of the degree to which sleep disorders are problematic and impact children. Additionally, data from preliminary research indicates that sleep disorders nega tively impact behavior as well as daily functioning. This study further ex amined these relationships. Organization of remaining chapters The remaining chapters present informati on that is pertinent to this study. More specifically, Chapter 2 provides a thorough review of the related lite rature, discussing the five major sleep disorders seen in children and the relationship be tween these disorders and internalizing behaviors, externalizi ng behaviors, and adaptive skill functioning. Furthermore, in order to understand sleep prob lems, normal sleep is discussed as well as developmental differences both in sleep patter ns and in the presence of internalizing and externalizing behaviors. Chapter 3 details the methods that were used in this study, including sampling, instrumenta tion, procedures, and data anal ysis. The results of this study are discussed in Chapter 4, followed by a discussion of the results and their implications in Chapter 5.
11 Chapter 2 Review of the Literature Overview This chapter provides a review of the lit erature relevant to this study. Pediatric sleep disorders are discussed, including the pr evalence of sleep disorders in children and the characteristics of five major sleep diso rders most commonly seen in children. Normal, healthy sleep is described in some detail so that problematic sleep can be distinguished from healthy sleep. A thorough review of the literature focusing on the relationship between sleep disorders, adaptive functioning, and behavioral outcomes is presented. The chapter concludes with the rationa le for the current investigation. Introduction Sleep is a behavioral state th at is a natural part of ever y individualÂ’s life. In fact, people spend about one-third of their lives as leep. Sleep is considered problematic when Â“sleepiness interferes with daily routines a nd activities, or reduced ability to functionÂ” (National Heart, Lung, & Blood Institute, NI H, 2006). Although a definition of sleep problems in children does exist, objective, reliable, and cost-effective measures of sleepiness and alertness in children are la cking. Subjective self-report data regarding sleepiness are largely unavailable in children. Behavioral manifestati ons of sleepiness not only vary with age and developmental level but also are often not reliably interpreted by parents and other caretakers. As a result, the amount of research on pediatric sleep disorders is less than that of adults. In the 1991 edition of Sleep Research (Chase, Lydic,
12 & OÂ’Connor, 1991), a culmination of publishe d research on sleep was evaluated and revealed that sleep research involving child ren ages 5 to 12 years accounted for only 7% of the sleep research done with humans, and studies involving toddlers ages 3 and 4 only accounted for 3% of all sleep research conduc ted. Studies that have been conducted on pediatric sleep problems report an overall prevalence of a va riety of parent-reported sleep problems ranging from 25Â–50% in preschool-a ged samples to 37% in a community sample of 4Â–10 year-olds (Owens, Spirit o, Mc-Guinn, & Nobile, 2000; Kerr & Jowett, 1994; Mindell, Owens, & Carskadon, 1999). A survey conducted by Johnson (1991) found that 42% of 12to 35-month-olds s howed problematic bedtime resistance, and 35% woke and demanded attention at night. Other research has shown that bedtime struggles become increasingly common from th e second to the fifth year (Beltramini & Herzog, 1983; Crowell, Keener, Ginsburg, & An ders, 1987), and can still be a problem for more than 25% of children ages 5 to 12 years (Blader, Koplewicz, Abikoff, & Foley,1996). Mindell et al (1994) found that practicing pe diatricians noted that 23% of their patients, between the ages of 6 months and 4 years, experienced sleep problems that were brought to the attenti on of the physician. While prevalence rates of sleep disturbances in children are being resear ched, the prevalence of sleep disorders in children are still unknown. Little is known about pediatric sleep disorders and how to diagnose them. Therefore, it is estimated that only 1-to-2% of children with sleep disorders are being diagnosed and treated. Of the children who ar e diagnosed as having a sleep disorder, 12 Â– 15% of them have learning, behavior, and/or emotional regulation challenges (National Institute of Health, 2001). Because sleep ha s been found to have such a profound impact
13 on daily functioning, it is extremely problema tic that the prevalence rates of sleep disorders in children are still unknown. Normal sleep In order to understand sleep problems, it is important to be able to distinguish healthy, normal sleep from problematic sleep. This next section describes healthy sleep and the biological mechanisms that cont ribute towards sleep. There is a common misconception that the brain is inactive during sleep, but this is not the case. Sleep is a dynamic, active process that involves physiolo gical changes in the organs of the body. Normal sleep has five stages; the first four stages of sleep are non-rapid eye movement (NREM) sleep and the last stage is rapid eye movement (REM) sleep. These five stages are cyclic, meaning that a slee ping person will go through th e five stages in the same order every 90 Â– 110 minutes. Typically, people cy cle through the five stages four to six times a night. The first four stages of sleep are NREM sleep, which people enter as they begin to fall asleep (Salzarulo & Fagioloi, 1995). Stage one of sleep is described as being between an awake state and a sleep state. At this stage, people drift in and out of sleep, and can be awakened easily. During stage one, peopleÂ’s ey es move slowly and their muscle activity slows down. Stage one typically lasts five to fifteen minutes per sleep cycle, and includes short dreams and myoclonic jerks (sudden mu scle twitches without any rhythm or pattern) often followed by a se nsation of starting to fall. These sudden movements are similar to the "jump" people make when star tled. If someone is woken during stage one, they often remember fragmented visual images.
14 Stage two is described as li ght sleep and is considered to be the onset of sleep. Humans spend 50% of their time sleeping in this stage of sleep. The average time for stage two sleep is fifteen to twenty minutes. During this stage, people are disengaged from their surroundings, but can still be easily awoken. Physiologically, body temperature drops, eye movements stop, and br ain waves slow down, but have occasional bursts of rapid waves called sleep spindles. St age three of sleep is deep sleep and is the stage in which the brain waves become even slower, but are punctuated by smaller, faster waves. Stage four of sleep is slow-wave deep sleep. In this stage, the brain produces only slow waves. Stage four is the deepest, mo st restorative sleep. During stage four, blood pressure drops, breathing is slow, tissue gr owth and repair occurs and hormones essential for growth are released. In both stages three and four of sleep, it is very difficult to wake someone. If a person is woken during stages three or four, they will most likely feel groggy and disoriented. It is in stage four that bedwetting, ni ght terrors, or sleep walking occurs. People with normal sleep patterns sp end 75% of their sleep time in stages one through four NREM sleep. The deepest stages of NREM sleep occur in the first part of the night and the episodes of REM sleep are longer as the night progresses. By morning, people spend nearly all their sleep ti me in stages one, two, and REM sleep. REM sleep, which is the fifth stage of sl eep, consumes 20 Â– 25% of nightly sleep (Morrison, 2004). The first occurrence of REM sleep is seventy to ninety minutes after falling asleep, and recurs about every ni nety minutes. The reason REM sleep is distinguished from NREM sleep is because of the physiological state of the body during this sleep stage. During REM sleep, eyes ra pidly dart back and forth; breathing is irregular, shallow, and fast; h eart rate increases; bl ood pressure rises; th e brain is active;
15 and dreams occur (Anders, Sadeh & Appare ddy, 1995). REM sleep provides energy to the brain and the body, and it s upports daytime performance. A number of factors contribute to healt hy sleep. Adenosine is a nucleoside that contributes to sleep. While awake, adenosine concentrations accumulate in the extracellular space of the basal forebrain. The more time a person spends awake, the more adenosine is built up. When a person sleeps, adenosine concentrations are broken down or reduced. Adenosine is thought to be responsible for keeping track of lost sleep and triggers the body when more sleep is needed (Basheer, Strecker, Thakkar & McCarley, 2004). Because of this built-in molecular feedback, people cannot adapt to getting less sleep than their body needs. Another contributing factor to sleep and sleepiness is the circadian clock (Dotto, 1990). The circadian clock is located in th e suprachiasmatic nucleus (SCN) of the hypothalamus in the brain. The SCN is an extr emely small structure consisting of a pair of tiny regions, each containing about 10,000 neurons out of the brainÂ’s estimated 100 billion neurons. These neurons are light sensi tive. Photoreceptors in the retina transmit light-dependent signals to the SCN, which th en signal different parts of the brain. The pineal gland, responsible for the production of melatonin, receives these signals from the SCN. Melatonin is a hormone that causes drowsiness. When it becomes dark, the production of melatonin is trig gered. Besides triggering melatonin, the SCN also impacts other physiological functions including body temperature, hormone secretion, urine production, and changes in blood pressure. If th e circadian clock is malfunctioning, there are a number of adverse effects that might o ccur such as changes in cerebral blood flow,
16 abnormal hormone levels, as well as physio logical changes in the brain, which may negatively impact healthy sleep (Ferber, 1996; Sheldon, 2005). The relationship between sleep and development Age affects sleep more than any other natural factor. Adults spend almost onehalf of total time sleeping in stages one a nd two of NREM sleep. In contrast, infants spend half or more of their total sleep time in REM sleep. Actually, infants fall directly into REM sleep and do not undergo the same sleep cycle that children and adults experience. A major task of a newborn is to organize the behaviors of wake, NREM, and REM, into discrete states (C arskadon, Anders & Hole, 1968). It is not until several weeks after they are born that infants are able to operate within a circadian rhythm. By six weeks of age, infants have a clear diurnal/ nocturnal pattern of sleep (Anders & Keener, 1985); and by six to nine months most in fants have a well-established pattern of nocturnal sleep (Moore & Ucko, 1957). As children grow older, the REM-NREM cycle lengthens. Kahn et al. (1973) conducted research comparing the sleep of two-year-old children to five-year-old children. This study found that five-year-olds have longer sust ained stages three and four of NREM sleep, while two-year-olds sp end more time in REM sleep. This study contributed to the evidence that during ear ly childhood, the sleep cycle is undergoing changes. While the Kahn et al. study does provide evidence for developmental differences in sleep, detailed research on sl eep in pre-school aged children is lacking. Thus physiologically, a lot remains unknown about sleep in young children. What is known is that as children grow older, stage two of NREM sleep incr eases. Gradually, as
17 children mature, the percentage of total sleep time spent in REM progressively decreases to reach the one-fifth level typical of later childhood and adulthood. The amount of sleep needed per day also di ffers by age. Infants need fourteen to fifteen hours of sleep a day while toddlers and pre-school-aged child ren need eleven to fourteen hours of sleep daily (Wolfson & Ca rskadon, 1998). It is important to note that for children pre-school aged or under, cumu lative sleep throughout a 24-hour time cycle is what is important. It is not expected that an infant or toddler will sleep over eleven hours at one time. Time spent na pping contributes to the tota l time of sleep in a 24-hour time period. Conceptualizing sleep needed per day changes for older children. Elementary school-aged children require ten to eleven hours of sleep a day, while adolescents and adults need nine hours of sleep a day (Wolfson & Carskadon, 1998). For anyone over pre-school-age, nap time is not counted towards total time of sleep per day. It is expected that elementary schoolaged children have at least ten hours of uninterrupted sleep per night, and adolescents and adults ha ve at least nine hours of uninterrupted sleep per night (Wolfson & Carskadon, 1998). The amount of time spent sleeping is not the only thing that cont ributes to healthy sleep. In fact, it is possible th at an individual has the proper quantity of sleep, but their sleep quality is poor. Sleep qua lity is extremely important to consider when assessing sleep. During normal, healthy sleep, the brain triggers physiological responses, such as respiratory functioning, and cardiovascu lar functioning (She ldon, 2005). If an individualÂ’s sleep quality is poor, this can lead to physiological malfunctioning as discussed previously, which in turn can negatively effect sleep (Rosen, 2005). Understanding healthy sleep helps to differentiate problematic sleep.
18 Measures to assess sleep disorders In order to comprehend and assess problem atic sleep, sleep must be measured. Sleepiness or feeling tired needs to be quan tified because subjective reports of sleepiness can be unreliable (Thorpy et al., 2006). There are a number of methods to assist in the assessment of sleep. The two most comm only used methods are polysomnography and multiple sleep latency test (MSLT) (Dave y, 2005). A polysomnogram, also referred to as a sleep study, measures physiologic paramete rs related to sleep and wakefulness. Polysomnograms are overnight tests that extensively measure sleep architecture, including time in bed and total sleep time; sleep efficiency and latency; REM latency; the percentage of time spent in each sleep stag e; arousals; and sleep fragmentation. Sleep architecture is measured by electroence phalography (EEG), electromyography (EMG), electroocculogram (EOG), thermocouple sensor s, piezo crystal effort sensors, pulse oximeter, and actigraphy. EEG is an electrical recording of the brain that detects and records brain waves to determine the stage of sleep during any given period of the night. EMG records electrical activity in muscles, which is helpful in documenting arousals and spastic movements. EOG records eye movements during sleep and also helps determine sleep stages. Thermocouple sensors measur e airflow through tracki ng the amount of air moving in and out of airways. Piezo crys tal effort sensors measure chest wall and abdominal movements during breathing. Both thermocouple sensors and piezo crystal effort sensors are used to determine the pr esence and extent of how often a person stops breathing in their sleep. The pulse oximet er measures oxygen saturation. Actigraphy records physical motion. All of these tests are conducted si multaneously while a person sleeps. The polysomnogram recordings are divi ded into epochs or thirty second time
19 intervals. For every epoch, the predominant stag e of sleep is recorde d. The total time and relative proportion of time spen t in each stage of sleep is calculated and latencies to REM sleep are recorded. Abnormal neurophysiological events, respiratory activity, and other parameters like body position are also recorded. To assess daytime sleepiness and the amount of time it takes to go from awake to sleep, MSLT is used. MSLT is usually conducted following a polysomnography, and measures the average time it takes to fall asleep. In an MSLT, EEG, EOG, EMG, and heart rate are measured as we ll as sleep latency. Sleep latenc y is defined as the time from intent to fall asleep to the first epoch of a ny sleep stage. An average time to fall asleep is ten minutes. If someone falls asleep in less th an five minutes or ca nnot fall asleep after twenty minutes, that indicates a probl em. MSLT and polysomnography are used to diagnose sleep disorders. Pediatric sleep disorders Pediatric sleep disorders can be divided into four broa d categories; primary sleep disorders including dyssomnia and parasomnia, sleep disorders related to another medical disorder, sleep disorders due to a genera l medication condition, and substance-induced sleep disorders (Anders & Eiben, 1997). There are more than eighty different types of primary sleep disorders, but the most common sleep disorders in children are Periodic Limb Movement Disorder (PLMD), Res tless Legs Syndrome (RLS), Narcolepsy, Delayed Sleep Phase Syndrome (DSPS), and Obstructive Sleep Apnea Syndrome (OSAS). The following sections will review each sleep disorder in more detail. Periodic Limb Movement Disorder. Periodic Limb Movement Disorder (PLMD) is the periodic movement of the arms and/ or legs during sleep (C occagna, 1990). In order
20 to be diagnosed with PLMD, a person must move at least five times for every hour of sleep and the movements must interfere with sleep (Picchiette, England, Walters, Willis, & Verico, 1998). Four contractions lasti ng between 0.5 and 5 seconds every four to ninety seconds constitute a movement (Hen ing, 1999). The most common movements in PLMD are a flexed knee or hip, the extension of the big toe, and the flexing of the foot. Children with PLMD have increased stages one and two of NREM sleep and decreased stages three, four, and REM sleep (Trenkwalder, Walders, & Hening, 1996). Polysomnography and actigraphy are used to diagnosis PLMD. Medications, such as opioids, benzodiazepines, and anticonvulsants are used to treat PLMD (Hening, 1999). Other treatments for PLMD include sleep hygiene improvement as well as special diets. Only a few studies have been conducted on the treatment of PLMD in children, and studies on the long-term effects of medications used to treat PLMD in children are sparse. Prevalence of PLMD in children is still unknown (Owens, 2005). Restless Legs Syndrome. The diagnostic criteria fo r Restless Leg Syndrome (RLS) is uncomfortable sensations in the legs that are temporarily relieved by movement (American Sleep Disorders Association, 1997). Children with RLS describe the sensations as Â“snakes in the legsÂ” or Â“cr eepy-crawly things in the legsÂ” (Walters, Picchietti, Ehrenberg, & Eagne r, 1994). The most prominent symptom of RLS is the uncomfortable leg sensations, but other co mmon symptoms of RLS include muscular weakness, headaches, and daytime sleep iness (Coccagna, 1990). Symptoms are exacerbated by long periods of sitting, and te nd to be strongest around bedtime. RLS is associated with PLMD, and can be diagnosed alone or comorbidly (Coleman, 1982). The diagnosis of RLS in children is rare. It is hypothesized that many children who are
21 misdiagnosed with AttentionDeficit/Hyperactivity Disord er (ADHD) actually have undiagnosed RLS (Walters, Picchietti, Ehrenbe rg, & Eagner, 1994). The behavior that children exhibit when they have RLS looks ve ry similar to the di agnostic criteria of ADHD. Like PLMD, RLS can be treated through opioids, benzodiazepines, and anticonvulsants. Improved sleep hygiene and a sp ecial diet are also treatments for RLS. With mild cases of RLS, exercise, leg massages, and elimination of caffeine are recommended. Delayed Sleep Phase Syndrome (Circadian Rhythm Disorder). Delayed Sleep Phase Syndrome (DSPS) is the inability to fall asleep and wake up at regular hours for at least six months. Adolescents who do not fall asleep until after midnight and sleep until the afternoon are suspected of having DSPS (Roehrs & Roth, 1994). In fact, most of the studies conducted on DSPS use adolescents. Ther e is little research on children with DSPS. Poor sleep hygiene and a faulty ci rcadian clock are thought to cause DSPS (Caskadon, Wolfson, Acebo, Tzischinsky & Seif er, 1998). Chronotherapy is a treatment for DSPS which resets the circadian rhythm by imposes a bedtime ear lier and earlier until the bedtime reaches a normal hour (Czeisler et al., 1981). Narcolepsy. Narcolepsy is a neurological diso rder associated with excessive daytime sleepiness, cataplexy, and prematur e onset of REM sleep (Aldrich, 1992). It usually becomes evident during adolescents or young adulthood, with the peak age of symptoms occurring between the ages of fi fteen and twenty-five (Guilleminault, 1994). Daytime sleep attacks may occur with or w ithout warning and nighttime sleep may be fragmented. People with narcol epsy have trouble staying awake. There are three classic symptoms of narcolepsy: cataplexy, sleep paralysis, and hypna gogic hallucinations.
22 Cataplexy is a sudden decrease in muscle tone triggered by emotions such as anger, laughter, or surprise (Guilleminault, Mignot & Partinen, 1994). Severe attacks of cataplexy may result in a complete body collaps e with a fall to the ground and risk of injury. Milder forms of cat aplexy are more common and involve symptoms such as a dropping head, sagging jaw, slurred speech, buc kling of the knees, or weakness in the arms. Cataplectic attacks can last anywhere from a few seconds to a few minutes. Some narcoleptics have catapletic attacks daily wh ile others might only have a few attacks a year. Sleep paralysis is the temporary inability to talk or move when waking up or falling asleep, although being fully aware of th e surroundings (Guilleminault, 1994). The duration of these episodes may be from s econds to minutes. Breathing is maintained although some patients may experience a fright ening sensation of Â“not being able to breathe.Â” Hypnagogic hallucinations are visual and auditory di sturbances that occur while falling asleep (Guilleminault, 1994). The hallucinations te nd to be vivid, frightening, disturbing, and/or bizarre for the people havi ng them. The sleep cycle in narcoleptics is atypical and starts with REM sleep rather than NREM sleep. Narcolepsy can be diagnosed from polysomnography, MSLT, or human leukocyte antigen (HLA) testing. Diagnosing children is challenging because th ey do not present all narcolepsy symptoms, and these children are commonly misdiagnosed with psychiatri c disorders (Dahl, Holttum & Trubnick, 1994). Stimulants and antidepre ssants are the most commonly used treatment for narcolepsy. Obstructive Sleep Apnea Syndrome. Obstructive Sleep Apnea Syndrome (OSAS) accounts for 50% of sleep disorders and occurs in people of all ages from infants through senior citizens (Benbadis, 1998). OSAS is ch aracterized by the cessati on of breathing for
23 more than five seconds two or more times an hour during sleep. Apneic episodes cause temporary drops in blood oxygen and increa ses in carbon dioxide levels, which wake people up from sleep. Waking up from apneic episodes account for the chronic sleep deprivation and the resultant excessive daytime sleepiness that is a major hallmark of this condition. The cause for OSAS is usally an obstruction of the airway. Common obstructions to airway passage are tonsils, adenoids, the t ongue, palatal size and position, and the jaw (Bower & Buckmiller, 2001). Obesity is also highly comorbid with OSAS. Raspy breathing, snoring, gasping, and choking at night are all symptoms of OSAS. Other symptoms include ope n mouth breathing any time of the day and sleeping in strange positions at night. A deleterious outcome of OSAS is hypoxemia. Hypoxemia is caused by the lack of oxygen from an apneic episode, and results in problems with the nervous system and cognitive impairments (Findl ey, 1989). It is estimated that 1 3% of children have OSAS (Gaulter, 1992; Kuppe rsmith, 1996; Marcus, 1997; Wang, Elkins, Keech, Eauguier, & Hubbard, 1998), with a peak prevalence in children ages 3 to 7 years (Brouillette, Fernbach, & Hunt, 1982). The most common treatment for children with OSAS is the removal or their tonsils and/ or adenoids (Gaultier, 1992). According to Kohler (2004), 85% of children are cured of OSAS when their tonsils or adenoids are removed. Another common treatment for OSAS in both children and adults is the use of continuous positive airway pressure (CPAP), wh ich is a special oxygen mask that forces air into the throat. Other treatments for OS AS are weight loss and the use of dental appliances.
24 Deleterious effects of slee p disorders on young children Sleep problems may have significant s hortand long-term consequences on young childrenÂ’s cognitive, behavior, academic and so cial functioning, as we ll as their health (Anders, Carskadon, Dement, & Harvey, 1978; Fallone, Owens, & Deane, 2002). Daytime sleepiness resulting from fragmented or disturbed sleep is often manifested in young children by behaviors such as increased activity, aggression, impulsivity, acting out behavior, poor concentration, and inat tention (Carskadon, Pueschel, & Millman, 1993; Guilleminault et al., 1982). Recent research suggests that sleep problems in early childhood may predict the development of s ubsequent internalizing disorders in adolescence and adulthood (Gregory & OÂ’Connor, 2002). Sleep problems are associated with a va riety of changes in mood and behavior (Ali, Pitson, & Stradling, 1993) and disturbances of mood an d behavior can alter sleep. Sleep disturbances in pediatric special populations are so common that almost all psychiatric disorders in children are asso ciated with sleep disruption (Owens, 2005; Roberts, 2003). Many psychiatric disorders can be associated with fatigue, daytime sleepiness, abnormal circadia n sleep patterns, nightmares, and movement disorders during sleep. Growing evidence suggests that insomnia with no concurrent psychiatric disorder is a risk factor for later devel opment of psychiatric co nditions, particularly depression and anxiety disorders (Owens, 2005). Sleep problems also are associated with behavioral problems (Minde et al., 1993). Ch ildren diagnosed with ADHD tend to exhibit behavioral problems at bedtime including ta king a longer time to fall asleep, being tired upon awakening, having variable sleep durati on, and frequently waking up in the middle of the night (Corkum, Beig, Tannock, & Moldof sky, 1997). In order to further understand
25 how sleep impacts mood and behavior, unders tanding internalizing and externalizing behaviors is important. Defining internalizing a nd externalizing disorders Research has identified internalizing and externalizing problems as two broad dimensions of child psychopathology (Mash & Barkley, 2003). Exte rnalizing behaviors can be thought of as disruptive behavior disorders of childhood (Mash & Barkley, 2003). Achenbach (2001) identifies aggression, soci al problems and attention problems as subdimensions of externalizing behaviors. The Diagnostic and St atistical Manual of Mental Disorders Â– Fourth Edition, Text Re vision (DSM-IV-TR) (A merican Psychiatric Association, 2000), disorders including Atte ntion-Deficit Hyperactivity Disorder (ADHD), Oppositional Defiant Disorder (ODD) Conduct Disorder (CD), and substance abuse disorders are al l externalizing disorders. (Northey, Wells, & Silverman, 2003). Internalizing behaviors can be thought of as negative feelings or mood states that are directed towards oneself (Mash & Ba rkley, 2003). Achenbach (2001) identified withdrawn behavior, somatic complaints, anxious/depressed f eelings, and thought problems as subdimensions of internalizi ng behaviors. According to the DSM-IV-TR (American Psychiatric Association, 2000), in ternalizing behavior problems are most commonly equated with anxi ety and mood disorders. Developmental differences in psychopathology There are certain disorders that are not typically diagnosed for children under the age of five because of developmental differe nces in the presence of symptoms (Mash & Barkley, 2003). This is particularly rele vant to the current study since the sample population will be ages 24 months Â– 5 years. In mood disorders, such as depression,
26 having a depressed mood is one of the dia gnostic features of depression. But in young children, irritability, un cooperativeness, apathy, and disi nterest is more common than having a depressed mood (Kashani, Holcomb & Orvaschel, 1986). Similarly, preschoolers are unlikely to report feelings of hopelessness and dysphoria (Ryan et al., 1987); instead they may display a depressed appearance (Carlson & Kashani, 1988). In fact, depressed preschoolers are more likely to act out physically or report exaggerated somatic complaints than exhibit typical symp toms of depression (Kashani, Rosenberg, & Reid, 1989). Overall, the lifetime rate of depr essive disorders is less than 3% in schoolage children, and is even less common am ong preschool-age groups (Costello & Angold, 1995). Anxiety disorders, on the other hand are among the most common psychiatric disorders affecting children (Costello & Angold, 1995; Gurley, Cohen, Pine, & Brook, 1996; Mash & Barkley, 2003). In the DSM-IV -TR (American Psychiatric Association, 2000), children can be diagnosed with any of nine anxiety disorders including: separation anxiety disorder (SAD), panic disorder, a goraphobia, generalized anxiety disorder (GAD), social phobia, specific phobia, Obse ssive Compulsive Disorder (OCD), posttraumatic stress disorder (PTSD), and acu te stress disorder. These disorders share anxiety as the predominant feature, but ar e distinguished based on the focus of the anxiety (Mash & Barkley, 2003). Separation anxiety disorder is an anxiety disorder unique to children (APA, 2000). The major feature of SAD is the onset of ex cessive anxiety and fear regarding separation from home or from those to whom the child is attached (APA, 2000) Such anxiety must be inappropriate for the childÂ’s age and developmental level, especially because
27 separation anxiety is normal from approximately 7 months to 6 years of age (Bernstein & Borchardt, 1991). SAD has an acute and early onset often occurring after a major stressor or at a period of developmental change (L ast, 1989). A change in school or starting school can be a trigger (Last, 1991). Ch ildren with SAD commonly have bedtime resistance and report recurrent nightmares characterized by separation themes (BellDolan & Brazeal, 1993). Just as internalizing disorder symptoms differ by age, so do the symptoms of externalizing disorders. In preschool aged ch ildren, externalizing disorders such as ODD and CD are rarely diagnosed. Oppositional and defiant symptoms are fairly common during the preschool years, which means that it would take extrem ely high and severe levels of such behaviors, in comparison w ith age and sex norms, to warrant diagnosis (Coie & Dodge, 1998). In fact, stubbornness, tant rums, and defiance are relatively typical for preschool aged children. The symptoms of CD, however, are not normative during childhood and it is extremely rare that CD be diagnosed in children under the age of 9 (Loeber, 1988; Moffitt, 1993; Patterson, 1993). It is believed that the ODD pattern could serve as a developmental precursor to CD (Loeber et al., 1991). Because both ODD and CD are rare in preschool-aged children, eval uating the externalizing behaviors identified by Achenbach (aggression, social problems and atte ntion problems) is more logical. It is common for preschool-aged children to be active; move from one activity to another; to act without forethought, and to respond on impulse to ev ents that occur around them often with emotional reactions being quite ap parent. This behavior becomes problematic when a child persistently displays levels of activity that are excessive compared to peers of the same gender, age group, and deve lopmental level (Mash & Barkley, 2003).
28 Internalizing disord ers and sleep problems The majority of research examining th e association between sleep problems and internalizing problems has focused on adults (G regory et al., 2005). Much of this research examined the association be tween insomnia and depressi on, and suggests that adultsÂ’ sleep problems forecasts depression (Bre slau, Roth, Rosenthal, & Andreski, 1996; Chang, Ford, Mead, Cooper-Patrick, & Kla g, 1997; Livingston, B lizard, & Mann, 1993; Weissman, Greenwald, Nino-Murcia, & De ment, 1997). For example, Ford and Kamerow (1989) found that adults who reported insomnia at two consecutive interviews were significantly more likely to develop a ne w case of major depression over the course of the next year than were those without insomnia. Furtherm ore, the risk of developing major depression was reduced for those whos e insomnia had resolved by the second assessment. Similar findings have been repor ted by other investigat ors (Breslau et al., 1996; Dryman & Eaton, 1991; Livingston et al., 1993; Weissman et al., 1997). Whether or not sleep problems forecast depres sion in children is less clear and is in need of further investigation. Prelimin ary research suggests that childhood sleep problems may predict the development of subsequent internalizing problems in adolescence (Gregory & OÂ’Connor, 2002). Childh ood risk indicators, such as parental loss, family conflict, and physical and sexua l abuse also have been identified for the development of later internalizing probl ems (e.g., Birmaher et al., 1996; Fergusson, Horwood, & Lynskey, 1996). Less attention, howe ver, has been paid to assessing the predictive associations between sleep problems and internaliz ing problems in children. Gregory and OÂ’Connor (2002) conducted a longitudinal study to examine the specificity, order of appearan ce, and developmental changes in the relationships between
29 sleep problems and both intern alizing and externa lizing problems in children. The sample included 490 children ages 4 Â– 15 years. Pare ntal ratings of sleep and behavior as measured by the Child Behavior Checklist (CBCL; Achenbach, 1991) were obtained. The CBCL includes measures of externalizing beha viors, internalizing behaviors, and sleep problems. CBCL measures were collected annually over an 11-year period. Findings revealed that sleep problems were mode rately but significantly correlated with anxiety/depression, attention pr oblems, and aggression. Early sleep problems at age 4 years also predicted an increase in depre ssion/anxiety, inattent ion/overactivity, and aggression at mid-adolescence. For example, the correlation between sleep and anxiety/depression increased from r= 0.39 at age 4 to r= 0.52 in mid-adolescence. Overall, there was a sizable decrease in sleep problems from preschool to midadolescence. Other studies usi ng different definitions of sl eep problems and different age groups have found parallel associations (Gregory, Eley, OÂ’Connor, & Plomin, 2004; Wong, Brower, Fitzgerald, & Zuck er, 2004). It is noteworthy th at the prediction of later internalizing problems from sleep problems is more robust in adults than in children, suggesting that sleep problems may be a bett er predictor of inte rnalizing problems in older than in younger individua ls (Gregory et al., 2005). To expand on the Gregory and OÂ’Connor (2002) study, Gregor y, Caspi, Eley, Moffitt, OÂ’Connor and Poulton (2005) conducte d another longitudinal study to assess the relationship between childhood sleep problems a nd anxiety and depression disorders in adulthood. Parents of 943 childre n participating in a longitudi nal investigation of health and behavior provided information on their child renÂ’s sleep and internalizing problems at ages 5, 7, and 9 years. At both 5 and 7 year s, three sleep-related questions were asked
30 (Â“Sleep problems last night?Â”, Â“Typically ha s sleep problems?Â”, Â“Does child have sleep problems?Â”). At the 9-year-old assessment, six sleep-related questions were asked (Â“Sleep problems last night?Â”, Â“Sleepi ng difficulties?Â”, Â“Child has trouble falling asleep?Â”, Â“Child awakens at night and canÂ’t re turn to sleep?Â”, Â“Child slept much more recently?Â”, Â“Child wakens very early?Â”). The responses to the questions were coded on a binary scale (0 = no problem; 1 = sign of a problem). Internalizing problems were assessed by parent report usi ng the Rutter Child Behavior Scales (Rutter, Tizard, & Whitmore, 1970) at 5, 7, and 9 years. When th e participants were 21 and 26 years, adult anxiety and depression were diagnosed using a private st andardized interview (The Diagnostic Interview Schedule; Robins Cottler, Bucholz, & Compton, 1995), administered by interviewers unaware of pa rticipantsÂ’ previous data, including their mental health status. At age 21 years, disord ers were diagnosed usi ng the (then current) Diagnostic and Statistical Ma nual of Mental Disorders Â– Third Edition Revised (DSMIII-R) (American Psychiatric Association, 1987) criteria. At age 26, the DSM-IV (American Psychiatric Association, 1994) wa s used to determine the presence of a diagnosis. Findings showed that children w ith persistent sleep problems had more internalizing childhood problems than those with out persistent sleep problems. Of those children providing data on sleep problems at 9 years old, 97% of these participants also provided data on anxiety and depression in adulthood. Of the childre n with persistent sleep problems, 46% of them had anxiety in adulthood compared to 33% of adults with anxiety who did not have pers istent sleep problems duri ng childhood. This difference was found to be statistically significant. In c ontrast, there were no differences in the proportions of participants with and without persiste nt sleep problems who had
31 depression in adulthood. The results of th is study provide some evidence for a link between sleep problems and anxiety, although th ese results do not imply that early sleep problems predict an increase in later a nxiety. While these studies evaluated the longitudinal associations between childhood sl eep problems and the likelihood of later being diagnosed with an inte rnalizing disorder, neither study examined the relationship between having a pediatric sleep disorder as a young child and exhi biting internalizing behaviors as a young child. Paavonen et al. (2002) ev aluated associations be tween sleep problems and psychiatric symptoms of 4,531, 8 and 9 year old children. The ChildrenÂ’s Depression Inventory (CDI) was used as a self-report questionnaire, while parents completed the Rutter A2 (RA) scale, which is a 36-item questionnaire assessing psychiatric symptoms. Teachers of the children completed a RA scal e for teachers. These scales were used to assess both psychiatric sympto ms and sleep problems since both the CDI and RA include questions assessing sleep. Fi ndings showed that children with more severe sleep problems were more likely to have psychi atric disturbances including emotional problems, hyperactivity, and other behavioral problems. The limitation to this study was that sleep problems were assessed through m easures that have a limited number of questions assessing sleep disturbance rather than using a measure designed to assess risk for sleep disorders. Additionally, this st udy evaluated only 8 and 9 year old children, which potentially limits the generalizability of the findings to children of other age groups.
32 Externalizing disord ers and sleep problems There is substantial evidence that sleep problems in children are associated with behavior problems in pediatric samples (Broughton & Shimizo, 1995; Fallone, Ownens, & Deane, 2002; Owens, Opipari, Nobile, & Spirito, 1998). Children with identified behavior problems are more li kely to have parent-reported sleep problems than children without behavior problems (Richman, 1987; Richman, Stevenson, & Graham, 1982). For instance, Zuckerman, Stevenson, and Bailey (1987) found that children whose sleep problems persisted from age 8 months to 3 years showed relative ly high levels of tantrums and other management difficulties at age 3 years compared to controls. In a sample of children, with the mean age of 5 years, Owens-Stively et al. (1997) found that children diagnosed with sleep problems exhibited both negative emotional temperament as well as more disruptive behavi or when compared to controls. Owens, Opipari, Nobile and Spirito ( 1998) conducted a study that examined the relationship between sleep quali ty and daytime behaviors in 152 children ranging in ages from 2 through 12 years. All participants we re referred to a pediatric sleep disorders clinic and had primary diagnoses of either Obstructive Sleep Apnea Syndrome (OSAS) or Behavioral Sleep Disorder (limit setting sleep disorder). Parents of the participants completed three sleep questionnaires (ChildrenÂ’s Sleep Behavior Scale (CSBS), ChildrenÂ’s Sleep Habits Questionnaire (SHQ) and Obstructive Sleep Apnea Screening Questionnaire (OSASQ)) and a behavior rating scale (Eyberg Child Behavior Inventory (ECBI)). The participants of the st udy were categorized into one of three groups: (1) having only a behavioral sleep disorder, (2) having only OSAS, or (3) combined having both OSAS and a behavioral sleep disorder Findings showed that children in the
33 behavioral sleep disorder group had a greate r number and severity of externalizing daytime behavior problems as measured by the ECBI than the other two groups. The greatest limitation of this study is that each group being compared had a diagnosed sleep disorder, so it is hard to attr ibute externalizing disorders to poorer sleep quality alone. This study could have been stronger if a cont rol group of children with no diagnoses was also assessed and compared to children with sleep diagnoses. Chervin, Dillon, Archbold and Ruzicka ( 2003) examined the relationship between childrenÂ’s sleep quality and daytime behavi or. The sample included 872 children ages 2 through 14 years attending tw o general pediatric clinic s. The childrenÂ’s parents completed questionnaires assessing sleep a nd behavior including: a Pediatric Sleep Questionnaire and The ConnorÂ’s Parent Rating Scale. Behavioral differences between children with and without sleep-related proble ms were analyzed. Children rated as having aggressive behavior and behavi ors that reflect conduct proble ms were two to four times more frequent among children at high-risk for sleep disordered breathi ng or periodic limb movement disorder than among children with less aggressive behavior. Excessive daytime sleepiness was strongly associated w ith conduct problems as well. Overall, children at risk for sleep problems were more likely to exhibit challenging behaviors. One problem with this study is that problematic behavior was not differentiated by age. As discussed earlier, conduct disordered behavior is not typically seen in younger children. This study would have been stronger had the age range been narrowed, focusing on a particular age group. Lavigne et al. (1999) conducted a simila r study with preschool-aged children. In this study, parents of 510 children ages 2 to 5 years reported on th eir childÂ’s sleep and
34 behavior. To measure behavior, parents comp leted the Child Behavior Checklist and the Rochester Adaptive Behavior Inventory. Sleep was assessed by asking parents to report the usual time at which their child fell asleep and woke up, as well as the number of naps taken per week and the average length of naps. An estimate of average sleep in a 24-hour period (including nap time) was calculated. Fi ndings indicated that the less sleep the child had, the higher levels of externalizing behavior pr oblems the child exhibited, particularly aggressive behavi ors. The biggest limitation of this study is that sleep was assessed through informal measures. There are a number of sleep-related problems that can be overlooked when just asking parents what time they put their child to sleep and what time their child woke up. A number of st andardized sleep measures, including the Sleep Disorders Inventory for Students and the ChildrenÂ’s Sleep Habits Questionnaire, ask more specific questions that assess sleep quality in children. Using standardized sleep-measures can potentially strengthen stud ies that wish to evaluate childrenÂ’s sleep and its impact on childrenÂ’s overall functioning. Challenging behavior is not the only behavior associated with poor sleep quality in children. Children may engage in more risk-t aking behaviors when they have poor sleep quality, which could place them at greater ri sk for injury. Research conducted by Owens, Fernando, and McGuinn (2005) investigated th e relationship between sleep disturbance and both injury rates and injury-prone beha viors in 71 children ages 3 through 7 years enrolled in a pediatric clinic. The parents of the participants completed the ChildrenÂ’s Sleep Habits Questionnaire as well as the In jury Behavior Checklist. Additionally, each patientÂ’s medical chart was reviewed for re ported injuries includi ng falls, cuts and lacerations, unintentional inges tions, pedestrian injuries, bur ns and scalds, and choking
35 episodes. Results suggest that children with mo re frequent injuries had significantly more sleep problems overall than did children with low injury rates. Children with more parent-reported injury prone behaviors also had significantly more sleep disturbance. Daytime sleepiness-related items did not differ between injury history or injury behavior groups. Results of this study support an increas ed prevalence of sleep disturbances in young children with higher injury rates and more injury-prone behavior. Children diagnosed with Attention-Defic it Hyperactivity Disorder (ADHD) tend to be more injury prone. Considerable empiri cal evidence supports an association between sleep disorders in children and the diagnosis of ADHD (Chervin et al., 1997; Picchietti, England, Walters, Willis, & Verrico, 1998; St ores & Wiggs, 1998). Sleep problems, particularly difficulties in initiating and main taining sleep, are reported in an estimated 25% to 50% of children and adolescents with ADHD in clinical practice (Owens, 2005). Picchietti et al. (1998) examined the re lationship between meeting diagnostic criteria for ADHD and having PLMD and/ or RLS diagnoses. This study included 69 children ages 2 Â– 15 years who were being seen by a neurologist for ADHD-related symptoms. A neurological history and examina tion were performed, and all participants were diagnosed with ADHD. Parents of the pa rticipants were asked to observe their childÂ’s sleep for minimally three nights im mediately after their childÂ’s sleep onset. Parents who reported significant movement duri ng their childÂ’s sleep were referred for an overnight PSG. Of the 69 participants, 27 underwent an overnight PSG. Archival PSG data matched for age were used for the cont rol group (n = 38). Of the children who were diagnosed with ADHD and had an overnight PSG, 18 of them were also diagnosed with
36 PLMD. Only 2 out of the 38 matched controls met PLMD criteria. These results indicate a relationship between being diagnosed with PLMD and ADHD. Gaultney, Terrell, and Gingras (2005) compared the relative strength of ADHD symptoms with sleep disordered breathing, PLMD, and bedtime resistance behaviors. Parents of 283 children ages 7-14 years co mpleted the Pediatric Sleep Questionnaire (PSQ; Chervin et al., 2000). Besides inquiri ng about sleep, this questionnaire also has two questions asking parents whether or not their child has been diagnosed with ADHD by a professional. Using the results from the Pe diatric Sleep Questionnaire, risk for SDB, PLMD, and bedtime resistance behaviors we re correlated with ADHD symptoms. High correlations were found between parent-r eported ADHD symptoms and PLMD. These data, however, should be interpreted with cauti on due to the fact that only one measure was used that was highly dependent on pare nt report. There were no measures in the study solely assessing ADHD symptoms. Owens, Maxim, Nobile, McGuinn, a nd Msall (2000) conducted a study to determine the prevalence of pa rent-reported sleep disturbanc es with a sample of schoolaged children diagnosed with ADHD. The sa mple consisted of 46 children diagnosed with ADHD and 46 matched controls ages 5 Â– 10 years. An ADHD diagnosis using DSM-IV-TR criteria was determined by an eval uation team which included a behavioraldevelopmental pediatrician, a neuropsychologi st, a social worker, and an educational consultant. Every participant also had a tw o-day evaluation which included a physical and neurodevelopmental examination, a parent -child clinical inte rview, the CBCL, The Parent Child Depression Inventory (P-C DI; Kovacs, 1985), the Conners Parent Questionnaire, and a review of medical and sc hool records. Sleep related behavior was
37 measured using the ChildrenÂ’s Sleep Hab its Questionnaire (Owens, Spirito, & McGwinn, 2000) and the Sleep Self-report (Owens et al., 2000). Findings showed that children diagnosed with ADHD had significantly highe r sleep disturbance scores on both sleep measures than did controls. Additional findi ngs were that children diagnosed with ADHD had a shorter sleep duration compared to controls. This study s upports a relationship between ADHD and sleep problems. The strong link between ADHD and sleep di sorders is thought to be because the prefrontal cortex, the locus coeruleus, and neurotransmitters that regulate sleep also impact attention and arousal. Physiological dysfunctions result in deficient information processing, memory, and learning, as well as problems with self-regulation. Another possible connection is that sleepiness either coexists with or produces symptoms consistent with ADHD (Gaultney, Terrell, & Gingras, 2005). Children diagnosed with ADHD also have impairments in their daily functional skills. (Stein, Szumowski, Blondis & Roizen, 1995). Adaptive behavior in young children Adaptive skills are practical, everyday skill s needed to meet the demands of the environment (Harrison & Oakland, 2003). They are skills that a person learns in the process of adapting to his/he r surroundings. Examples of ad aptive skills include those related to dressing, eating, comm unicating with others, self-car e, social interaction, and practicing safety. The American Academy of Mental Retardation (AAMR) defines adaptive skills as Â“the collection of conceptual, social, and pract ical skills that have been learned by people in order to function in th eir everyday livesÂ” (p. 41). Adaptive behaviors can change according to a personÂ’s age, cultural expectations, and environmental
38 demands (Wikipedia.org, 2006). Since adapti ve behaviors are for the most part developmental, it is possible to describe a pers on's adaptive behavior as an age-equivalent score. An average seven-year-old, for exam ple, would be expected to have adaptive behavior similar to that of other seven-year-o lds. For instance, it would be expected that a typical American seven year ol d could dress him or herself. This adaptive behavior may not be expected of a three-year-old, or possi bly in another culture with seven-year-old children. Adaptive behavior assessment is typically used to aid in diagnosing or program planning. For example, the dia gnosis of mental re tardation requires deficits in both cognitive ability and adaptive behavior, occurring before age 18 years. Assessment of adaptive behavior is also used to determine th e type and amount of sp ecial assistance that people with disabilities may need. Adaptive behavior assessments are often used in preschool and special educa tion programs for determining eligibility, for program planning, and for assessing outcomes. Adaptive skills should be assessed routinely for children who have difficulties, disabilities, or disorder that interfere with daily functioning (Harrison, 1990; Harri son & Boney, 2002; Reschly, 1990). One adaptive skill that is commonly measured for younger children is selfregulation, which is considered to be a key fact or in childrenÂ’s adjustment. The ability to self-regulate underlies many of the behaviors and attributes associated with successful school transition and academic achievement (Coie, Dodge, & Kupersmidt, 1990; Ladd, 1990; Schultz, Izard, Ackerman, & Youngstrom, 2001). When a child has impaired selfregulation, this can result in less on-task behavior in the clas sroom (Goodman & Linn, 2003). On-task behavior in a classroom is imperative for learning (Strain, Danko, &
39 Kohler, 1995). Therefore, ch ildren who experience difficult y with the regulation of emotion and attention are at risk for experien cing problems with the adjustment to school. Problems with the initial adjustment to school can lead to continuing difficulties with social and academic competence and self-conc ept and increased risk for school failure (Blair & Peters, 2003). Blair and Peters (2003) conducted a study to examining 42 children ages 3 years 9 months to 5 years 7 months physiological a nd cognitive self-regulation, and to relate them to teacher reports of social compet ence and engagement in the classroom. All children were attending a Head Start facilit y. Participants were seen during half-hour increments over two days. The first da y, children completed the Peabody Picture Vocabulary Test Â– 3 (PPVT-3; Dunn & D unn, 1997). On the second day, children were administered electrocardiograms (ECG) to assess their vagal tone while completing executive functioning measures (a peg-tapp ing test and a stroop-like day and night measure). Parents then completed a CBCL, wh ile the childrenÂ’s teachers completed the Teacher Observation of Classroom Adapti ons Â– Revised (TOCA-R; Werthamer-Larsson, Kellam, & Wheeler, 1991). The TOCA-R is a 41-item measure designed to assess childrenÂ’s performance on showing concern for others, accepting limits, paying attention, staying on task, and showing effort. Results in dicated that higher resting vagal tone, as well as a vagal increase during the administration of the cognitive task, and higher levels of fearful emotionality on the CBCL were rela ted to higher ratings of social competence. In contrast, lower resting vagal tone, as we ll as a vagal suppressi on in response to the cognitive task, and higher levels of execu tive function were associated with higher teacher ratings of on-task behavior, adjusted for social competence. These findings were
40 unexpected because social competence and on-task behavior are typically strongly positively correlated and this studyÂ’s findings did not support this. This study had several limitations including the small sample size a nd the single time point of measurement. This study also only evaluated adaptive func tioning as it related to social competence, disregarding many other areas of adaptive functioning. A larger study conducted by Palermo et al. (2002) evaluated adaptive functioning more broadly. In this study, 14,630 children ages 6-15 years seen in three large pediatric practices were evaluated to determine the prevalence of functional limitations and to identify psychosocial factors related to functional limitations. Parents completed questionnaires including the Pediatric Symptom Checklist (to assess emotional & behavioral problems), the Family Apgar (to a ssess family functioning) and the Functional Limitations Index (to assess adaptive skill s). Findings showed that 15% of the participants had some limitation in thei r daily functioning. Schoolwork and physical function limitations were more common than pe rsonal and self-care. Children with any psychosocial symptoms (externalizing, intern alizing, and attention problems) were at increased risk for functional limitations. This study had the benefit of having such a large sample size. In addition, this study was able to identify specific areas of adaptive functioning that were more impaired over other areas. While there are studies examining the re lationship between adaptive skills and psychosocial symptoms, there are no studies evaluating the relati onship between sleep and adaptive behavior. Since there is emerging evidence linking sleep problems to internalizing and externalizing behaviors, and having psychosocial symptoms is associated with poorer adaptive skills, it is hypothesized that ther e will be a positive
41 relationship between poor adaptive skills and ha ving increased risk for a sleep disorder. Summary Research in the area of pedi atric sleep disorders is sti ll relatively new. The existing research has shown that sleep disorders can have deleterious effects on childrenÂ’s cognitive and behavioral outcomes. It is possible that many children who have sleep disorders are being misdiagnosed with other di sorders such as ADHD. It is important to continue researching pediatric sleep disorders since it is estim ated that a large number of children do in fact have undiagnosed and untre ated sleep disorders. There is no published research to date that assesses the relations hip between being at risk for various sleep disorders and the impact on adaptive f unctioning as well as internalizing and externalizing behaviors in young children. Likewise, very lit tle research exists on the prevalence of sleep disorders in young child ren. This study seeks to examine both the prevalence of sleep disorder risk in childre n ages 24 months Â– 5 years, as well as how being at risk for certain sleep disorders is a ssociated with the presence of internalizing behaviors, externalizing behavi ors, and adaptive functioning.
42 Chapter 3 Methods Introduction The purpose of this study was to assess the prevalence rates of a clinic-based sample of children ages 24 months Â– 5 years at risk for sleep disorders. In addition, this study examined the relationship between sympto ms of sleep disorders, internalizing and externalizing behavior, and ad aptive functioning in these children. This chapter presents information on the participants in this study, the instrument s and procedures that were used for data collection, and the methods of data analysis used in this study. Participant characteristics A sample of 104 children ages 24 months -5 years attending a university-based child development clinic served as participants in this study. The university-based child development clinic provides children and th eir families with collaborative evaluations, consultation, service coordination, referral, and family support services. All children attending the clinic receive a standard battery of assessments, and these data from these assessments were analyzed for the purpose of this study. The sample was 78% male and 22% female. The race/ethnicity of the ch ildren was 53.8% Caucasian, 3.8% mixed race, 30.8% Hispanic, 10.6% African-American, and 1% Native American. The ethnic breakdown of this sample is similar to th e ethnicity of the county as a whole. The childrenÂ’s ages ranged from 2 years to 5 y ears with a relatively equal sampling of each age (e.g, 27.9% of the sample were 2 years ol d, 26.9% of the sample were 3 years old,
43 23.1% of the sample were 4 years old, and 22.1% of the sample were 5 years old). A little over half of the participants had private insurance (56.7%), while 43.3% of the sample had Medicaid. See Table 1 for participant characteristics. Table 1 Participant Characteristics Race/Ethnicity N Percentage African-American 11 10.6% Caucasian 56 53.8% Hispanic 32 30.8% Native American 1 1.0% Mixed/Other 4 3.8% Insurance Type Private Insurance 59 56.7% Medicaid 45 43.3% Age 2 years old 29 27.9% 3 years old 28 26.9% 4 years old 24 23.1% 5 years old 23 22.1% Gender Male 81 77% Female 23 13% Setting All of the data for this study were co llected through USF Pediatrics Child Development Clinic located in West Central Florida. This clinic serves children from birth through the age of 12 y ears who live in the local comm unities. Children are usually referred to the child development clinic becau se an adult (e.g., pediatrician, teacher, parent) is concerned with thei r lack of progress in devel opmental, behavioral, social, and/or academic functioning. All children w ho come through the clinic have an initial triage appointment lasting approximately one hour where a team of professionals
44 interview the parents and screen the child fo r problems related to the referral concern. This team may include a physician or resi dent, a school psychologist or (school) psychology intern, and sometimes a nurse or nursing students. Based upon the information gained, the team of professionals decides if further assessments are needed. If developmental or academic functioning is of concern, parents are given a battery of assessments to complete including the SD IS-C, ABAS-II, and the CBCL. The child is then rescheduled for further applicable assessments. Parents complete the assessment battery provided during the tr iage appointment at home, and send the completed battery back to the clinic before th eir childÂ’s next scheduled a ppointment. Based on assessment results, intervention options are developed with the parents. Follow-up appointments are scheduled as needed on a case-by-case basis. Instrumentation Sleep Disorders Inventory for Students The Sleep Disorders Inventory for Students (SDIS-C; Luginbuehl, 2004) was developed as a school-based screening instrument used to identify children at risk for Obstructive Sleep Apnea Syndrome, Narcolepsy, Periodic Limb Movement Diso rder, Restless Leg Syndrome, and Delayed Sleep Phase Syndrome. There are two versi ons of the SDIS; one normed for children ages 2 Â– 10 years (SDIS-C), and the other form normed for adolescents ages 11 Â– 18 years (SDIS-A). Both the SDIS-C and SDIS -A are available in English and Spanish. This study examined data for children ages 24 months Â– 5 years, therefore only the SDISC was used for data collection. The SDIS-C is a 41-item questionnaire whic h asks parents to rate their childÂ’s sleep behaviors in the past 6 Â– 12 months. The first 30 responses are on a likert scale
45 ranging from 1 Â– 7, and the last 11 responses ar e in a yes/no format. For most parents, the SDIS-C takes approximately 8 Â– 15 minutes to co mplete. If a parent is unsure of how to respond to specific items, parents are prompt ed to observe their childÂ’s sleep on two different nights. Responses are scored using a computerized scoring program. Scores are expressed as T-scores with a mean of 50 a nd a standard deviation of 10, and yield sleep disorder risk levels. T-scores of 60 and unde r are in the normal ra nge of sleep meaning that the child is sleeping similarly to childre n of the same age. T-scores of 61 Â– 64 are in the caution range, meaning that the child is at mild risk of having a sleep disorder. A tscore of 65 and above places a child in the high-risk range of having a sleep disorder. Beyond producing t-scores, the computerized sc oring program also pr oduces a bar graph with scores for each sleep disorder and a sc ore for the total sleep disturbance index, as well as a very detailed interpretive report explaining the different sleep disorders and recommendations if a child is at risk for a particular sleep disorder. The SDIS-C was normed on a national samp le of 821 children with demographics similar to the 2000 U.S. census. Exploratory and confirmatory factor analyses were conducted yielding four sleep factors on th e SDIS-C, which includes OSAS, Excessive Daytime Sleepiness (EDS), PLMD, and DSPS. Criterion-related va lidity was moderate with OSAS being at 33% ag reement compared to polys omnography and respiratory distress index, and EDS being at 83% agreement compared to Multiple Sleep Latency Tests. Using discriminate function analysis, pr edictive validity of the SDIS-C was found to be 93%. An expert review panel determined that the content vali dity of the SDIS-C was 94%. The internal consistency was 0.91 a nd the test-retest validity was 0.97. Overall,
46 the SDIS-C has demonstrated an 86% accuracy rate in determining which children need to be referred for more comp rehensive sleep evaluations. Child Behavior Checklist The Child Behavior Checklist (CBCL; Achenbach, 2001) was developed to assess internalizing and externaliz ing behaviors in children. There are multiple versions of the CBCL. The CBCL/1 5 was normed for 18-month to 71-month olds and is filled out by parents or caregivers. The CBCL/6 Â– 18 was normed for children ages 6-18 years and is filled out by parents or caregivers. The CBCL problem behavior scores include two broad-band factors (internalizi ng and externalizing problems), a total broad-band score, and eight narrow-band subscales. The narrow-band subscales include aggressive behavior, anxi ous/depressed, attention problems, delinquent behavior, social problems, somatic comp laints, thought problems, and withdrawn behavior. All versions of the CBCL are availa ble in English and Spanish. Since this study evaluated the results of children ages 24 mont hs Â– 5 years, the CBCL/1 5 was used for data collection. The CBCL/1 5 is a 99-item questionnaire th at asks parents to rate their childÂ’s behavior based on the last 2 months, plus prompts for a description of problems, disabilities, what concerns parents most about their child, and the best things about the child. Parents are asked to rate each item as 0 for not true of the child, 1 for somewhat or sometimes true and 2 for very true or often true The CBCL/1 5 takes approximately 20 minutes to complete. Responses are scor ed using a computerized scoring program. Scores are expressed as t-scores with a mean of 50 and a standard deviation of 10. A tscore of 64 or under is in the normal range; 65 Â– 70 is in the borderl ine range; and 70 or above is in the clinical range. Scores in th e borderline or clinical range indicate that a
47 child has more problems than are typically reported by a child of the same age and gender. The CBCL/1 5 was normed on a national sample of 700 children. The manual reports median internal consistency coefficients for the Internalizing and Externalizing scales that range from .76 to .92. Studies of the CBCL subscales indicated high retest reliability (Withdrawn: r = .82; Somatic Complaints: r = .95; Anxious/Depressed: r = .86; Social Problems: r = .87; Internalizing Problems: r = .89) and adequate interrater reliability (Withdrawn: r = .66; Somatic Complaints: r = .52; Anxious/Depressed : r = .77; Social Problems: r = .77; Internalizing Problems: r = .66; Achenbach, 1991). Adaptive Behavior Assessment System Â– Second Edition The Adaptive Behavior Assessment System Â– Second Edition (A BAS-II; Harrison & Oakland, 2003) was developed to assess adaptive skills functioning fo r individuals birth to 89 years. There are multiple versions of the ABAS-II. The ABAS -II Parent/Primary Caregiver Form (Ages 0 Â– 5) was normed for children ages birth to 5 years 11 months and is filled out by parents or caregivers. The other ABAS-II forms include the Parent and Teacher Forms (Ages 5 Â– 21) and the Adult Form (Ages 16 Â– 89). No rm-referenced scores include three broad domains of adaptive behavior (Conceptual, Soci al, and Practical), a total score called the General Adaptive Composite (GAC), and 10 sk ill areas. The skill areas measured by the ABAS-II Parent/Primary Caregiver Form ar e communication, community use, functional pre-academics, home living, health and safety, le isure, self care, self -direction, social, and motor skills. The skill areas that make up the Conceptual domain are communication, functional pre-academics, and self-directi on. The Social domain is composed of skill areas that measure social skills and leisur ely skills. The skill ar eas that make up the
48 Practical domain are self-care, home living, co mmunity use, and health and safety. Both the ABAS-II Parent/Primary Caregiver Form (Ages 0 Â– 5) and the ABAS-II Parent Form (Ages 5 Â– 21) are available in English and Sp anish. Since this study evaluated the results of children ages 24 months Â– 5 years, the ABAS-II Parent/Primary Caregiver Form (Ages 0 Â– 5) was used for data collection. The ABAS-II Parent/Primary Caregive r Form (Ages 0 Â– 5) is a 241-item questionnaire that asks parents or caregivers to rate their childÂ’s current performance on adaptive skills functioning. Parents or careg ivers are asked to rate each item as 0 for is not able to do the skill, 1 for never or almost never when needed to do the skill, 2 for sometimes when needed will do the skill, and 3 for always or almost always when needed will do the skill. The ABAS-II Parent/Primary Caregiver Form (Ages 0 Â– 5) takes approximately 20 minutes to complete. Res ponses are scored using a computerized scoring program. Specific skill area scaled scores have mean of 10 and a standard deviation of 3. The skill area scores comb ine to form the three ABAS-II broad domain scores and the GAC score, each with a co mposite score mean of 100 and a standard deviation of 15. Composite scores falling between 90 Â– 109 and scaled scores falling between 8 12 are classified in the Average range. This study only used the GAC score to assess adaptive functioning rather than also using the broad domains of adaptive behavior (e.g., Conceptual, Social, and Practi cal) because the purpose of this study was to look at adaptive functioning as a general construct relative to sleep disorder risk. The ABAS-II Parent/Primary Caregiver Form (Ages 0 Â– 5) was normed on a national sample of 2,100 children ages birth to 5 years 11 months with demographics similar to the 2000 U.S. census. The manual reports the internal consistency for the skill
49 area scores to range from 0.80 Â– 0.92, and 0.91 Â– 0.97 for the composite scores. Studies of the ABAS-II subscales indicated high retest reliability (Communication: r = 0.82; Community Use: r = 0.79; Functional Pre-Academics: r = 0.85; Home Living: r = 0.83; Health and Safety: r = 0.81; Leisure: r = 0.80; Self-Care: r = 0.81; Self-Direction: r = 0.80; Social: r = 0.81; Motor: r = 0.80; Harrison & Oakland, 2003). In summary, the SDIS-C, CBCL, and AB AS-II are all psychometrically sound instruments, as evidenced by th eir validity and reliability. Each instrument has a different contribution toward providing information a bout a childÂ’s overall functioning. The SDISC assesses sleep quality and risk for various childhood sleep disorders, the CBCL measures problem behavior, and the ABAS-II assesses functional skills. Because these instruments do have strong technical propert ies and are commonly used in part of a standard battery of assessments at the clinic data from these instruments were used in this study. Procedure Permission was obtained to conduct rese arch with human subjects through the University of South Florida Institutional Revi ew Board (IRB). Because data are archival in nature, parental consent forms were not be necessary. The process by which the assessments were conducted was discussed in the Setting section of this chapter. After assessments results were obtained and reviewed with the childrenÂ’s parents, these data were entered into a clinic database. Participant data were entered into the database between February 2006 and February 2007. Perm ission from the Director of the child development clinic was granted for the student researcher to access these data for the purpose of this research. Confidentiality of participants was ensured because no names
50 were used on the database. Each subject had a unique number, which cannot be traced back to their name. Additiona lly, the student researcher had completed both IRB and HIPAA training. For each participant, th e researcher accessed their demographic information including gender, age, ethnici ty, and whether or not the family is on Medicaid, as well as the scores on th e SDIS-C, CBCL, and the ABAS-II from the database. Data were used from children be tween the ages of 24 months and 5 years 11 months. If the child was either below of the age of 24 months or above the age of 5 years 11 months, their data were not included in this study. There were 4 children between the ages of 24 months and 5 years 11 months who did not serve as partic ipants in this study because of missing data. Once incl usion criteria were met, part icipantsÂ’ scores were then transferred to a SPSS file where the data was analyzed. A school psychology graduate student conducted a data inte grity check by random sampling one -quarter of the data to ensure that the data were both entered and tr ansferred accurately from the database into SPSS. An inter-rater agreement of 98% was obtained. Data analysis Separate data analyses were conducted fo r each of the five research questions. Descriptive statistics were used to describe the demographics of the sample as well as additional information on the SDIS-C, CBCL and ABAS-II. The first research question sought to determine the prevalence rate of sleep disorder risk in children ages 24 months to 5 years who were assessed at the clinic. Pr evalence rates of sleep disorder risk were assessed through calculating a percentage with a 95% confidence interval. The second and third research questions ex amined the relationship between sleep scores in the normal, cautiona ry, and high-risk ranges and externalizing behavior and
51 internalizing behavior (respectively) as meas ured by the CBCL. An analysis of variance (ANOVA) was conducted for both research questi on to determine if there is a difference between means of the normal, cautionary, and high-risk ranges for sleep disorders. Follow-up tukey-tests were also conducted to determine which means differed significantly. The ANOVA assumptions of nor mality, homogeneity of variances, and independence were assessed for both research questions before the data analysis takes place. The fourth research question sought to exam ine the relationship between sleep scores in the normal, cautionary, and high-risk ra nges and GAC scores as measured by the ABAS-II. An ANOVA was conducted to determine if there is a difference between mean scores as measured by the SDIS-C. The a ssumptions of ANOVA were again analyzed before the data analysis took place. The final research question examined the relationship between sleep problems as measured by the CBCL and the sleep index scor e as measured by the SDIS-C. In order to assess this relationship, a 2-tailed Pearson Product Moment Correlation was calculated.
52 Chapter 4 Results Instrumentation reliability Sleep Disorders Inventory for Students. Parents completed the Sleep Disorders Inventory for Students Â– Childre nÂ’s version (SDIS-C). Parent s completed this screening measure as part of a standard battery of assessments administered at the Child Development Clinic. In this study, the reliabil ity estimate for the total sleep disturbance scale of the SDIS-C was meas ured using CronbachÂ’s alpha ( = 0.90). Child Behavior Checklist. The Child Behavior Checklist (CBCL) was completed by parents as part of a standard battery of assessments administered at the Child Development Clinic. The reliability es timate for the CBCL was measured using CronbachÂ’s alpha ( = 0.95). Adaptive Behavior Assessment System Â– Second Edition. Parents also completed the Adaptive Behavior Assessment System Â– Second Edition (ABAS-II) as part of a standard battery of assessme nts administered at the Child Development Clinic. The reliability estimate for the ABAS-II as measured using CronbachÂ’s alpha was 0.99. Descriptive statistics Upon examination of the sleep scores, it was found that the mean scores for the sleep disorders scales were in the low to mid 50s, except for the SDI Index score ( M = 60.42). The SDI Index mean score can be higher than all the other sleep disorder mean
53 scores when participants scor e above a 3 or 4 on two or more individual items within a sleep disorder risk scale. For instance, wh en a child is beginning to develop sleep problems, he or she may exhibit only a few sl eep symptoms that raise a particular sleep disorder risk scale a little above the mean of 50, but th ere are not enough symptoms yet to raise that scale into the caution or high risk range. However, when all the scores from each sleep disorder risk scale are combine d, they can raise the SDI Index score and indicate that overall, the ch ild is experiencing some em erging symptoms of sleep disorders but the child woul d not yet at risk for a pa rticular sleep disorder. For the SDISC, T-scores of 60 and under are in the normal range. The standard deviations for all sleep scores ranged from 9.45 to 13.0 points (see Table 2). On the CBCL, the means and standard deviations were as follows: 6 1.97 and 12.69 for the total CBCL score, 59.20 and 11.36 for the internalizing problems subscal e, and 61.54 and 13.30 for the externalizing problems subscale (see Table 3). A T-score on the CBCL of 64 or less is in the normal range. As for the adaptive functioning scores, it can be seen in Table 4 that the mean on the ABAS-II was 75.65 and the standard deviation was 19.20. On the ABAS-II, standard scores between 90 Â– 109 are in the average ra nge, scores between 80 Â– 89 are in the low average range, scores between 70 Â– 79 are in the borderline range, and scores of 69 and below are in the extremely low range. Prevalence of sleep disorders The first research question sought to de termine the prevalence rates of sleep disorders in the study population. According to the overall index of sleep disorders (SDI), 67.6% of children scored in the normal range of sleep. However, 12.4 % of children
54 received sleep scores in th e cautionary range, and 17.1% scor ed in the high-risk range. Frequencies for the specific types of sl eep disorders can be found in Table 5. When individual subscales were examined, it was found that 57.84% of the children scored in the normal range (T-sco res of 60 and under) acro ss all sleep disorders areas, including obstructive sleep apnea s yndrome, periodic limb movement, delayed sleep phase syndrome, and excessive daytime sleepiness (see Table 6). Further analysis revealed that 10.78% of the sample were reporte d as being at moderate risk (T-scores of 61 Â– 64) for having at least one type of sl eep disorder. The remainder of the children, 31.38%, were reported as being at high-risk (T -scores of 65 and above) for having at least one type of sleep disorder. Externalizing problems and sleep disorders The second research question sought to examine the relationship between normal, cautionary and high-risk range sleep disorder symptoms, as measured by the SDIS-C, and externalizing behavior problems, as meas ured by the CBCL. A T-score on the CBCL of 64 or less is in the normal ra nge; scores of 65 Â– 70 are in th e moderate range, and scores of 71 and above are in the clinical range. Th e distribution for each of these groups can be seen in Figure 1. The means and standard de viations for each gr oup on the externalizing problems scale are displayed in Table 7. Several assumptions were checked in order to ensure that an Analysis of Variance (ANOVA) was an appropriate test to use to determine whether or not differences existed between scores on the externalizing pr oblems subscale of the CBCL, based on the category of sleep disorder. Specifically, inde pendence, normality, and homogeneity were considered. Since all subjects were separate individuals who completed both measures
55 independently and without the ability to interact with each other, it was ensured that the assumption of independence was not violated. Sc ores on the externaliz ing subscale of the CBCL had a small positive skew (0.21), and kurtosis was normal (0.09), indicating that the assumption of normality was passed. Boxpl ots of each condition revealed that the high-risk sleep disorder category had the mo st variability of scores (SD = 16.03), while the caution sleep disorder category had the l east variability (SD = 8.80). However, this variation was not different enough for it to be problematic in conducting the ANOVA because the largest variance wa s not coupled with the smallest sample size (Stevens, 1995). 0.00.51.01.52.0SDIndex 40 60 80E x t e r n a l T S c o r e After checking all assumptions, a oneway ANOVA was conducted. The level of overall sleep disorder risk (normal, cauti on, and high-risk) served as the categorical variable, while the score on the externalizing problems subscale of the CBCL served as the continuous variable. Ther e was a statistically signifi cant difference among the three Figure1. Distr ibutionofexternalizingbehaviorsc oresbysleepdisorderrisklevel.
56 groups (F(2,99)=9.67, p < .0001). This indicates th at because the ANOVA was significant at the .05 level, there was a di fference in parent-reported externalizing problems based on the overall level of sleep disorder. A Tukey test was conducted in order to determine for which levels of overall sleep disorders risk (normal, cautionary, and high-risk) there was a difference in externalizing scores. The Tuke y test indicated a difference between the normal level and high-risk level of sleep disorders at a .05 confidence level. Th e difference between sample means was 14.17, with a 95% simultaneous confidence interval indicating that the difference between population means was between 6.45 and 21.90. No differences were found between the caution le vel and either of the other 2 leve ls of sleep disorders. Of the participants who were at high risk for a sleep disorder, 71% of these participants had CBCL externalizing scores falling in the clinical range, while 16% had CBCL externalizing scores in the borderline range and 13% had CBCL externalizing scores in the normal range. Of the participants who were at normal or no risk for a sleep disorder, 60% of these participants had CBCL external izing scores falling in the normal range, while 24% had CBCL externalizing scores in the borderline range and 16% had CBCL externalizing scores in the clinical range. Overall, the findings from the ANOVA a nd Tukey Test indicates that children who were rated as high-risk for a sleep diso rder received significantly higher scores on the externalizing problems subscale of the CBC L as compared to children who scored in the normal sleep range.
57 Internalizing problems and sleep disorders The third research questi on sought to examine the relationship between normal, cautionary and high-risk range sleep disorder symptoms, as measured by the SDIS-C, and internalizing behavior problems, as m easured by the CBCL. The distribution of internalizing problems for each of these groups can be seen in Figure 2. On the SDIS-C, T-scores of 60 and under are in the normal range, 61 Â– 64 are in the caution range, and 65 and above are in the high-risk range. On th e CBCL, T-scores of 64 and under are in the normal range, 65 Â– 70 are in the borderline ra nge, and 71 and above are in the clinical range. The means and standard deviations fo r each sleep disorders risk classification on the internalizing problems scal e are displayed in Table 8. 0.00.51.01.52.0SDIndex 40 50 60 70 80 90I n t e r n a l T S c o r e s Figure 2 Distribution of internaliz ing behavior scores by sleep disorder risk level
58 Table 2 SDIS-C Descriptive Statistics Mean Standard deviation Median Mode Skewness Kurtosis Min Max OSAS 54.51 10.52 51 45 1.15 1.13 41 88 PLMD 54.33 10.07 53 41 0.618 -0.278 39 80 DSPS 53.82 13.01 49 41 1.05 0.17 41 90 EDS 52.56 9.45 51 48 1.22 2.14 39 90 SDI 60.42 12.28 54 47 1.22 0.90 50 100 Table 3 t CBCL Descriptive Statistics Mean Standard Deviation Median Mode Skewness Kurtosis Min Max Total CBCL Score 61.97 12.69 62 65 0.141 0.228 32 90 Internalizing behavior 59.20 11.36 60 65 0.078 -0.015 33 89 Externalizing behavior 61.54 13.30 61 57 0.209 0.088 32 97 Table 4 ABAS-II Descriptive Statistics Mean Standard Deviation Median Mode SkewNess Kurtosis Min Max Adaptive functioning Skills 75.65 19.20 74 61 0.233 -0.735 41 123
59 Table 5 Prevalence of Sleep Disorders as Measured by the SDIS-C Level 1: Normal Level 2: Caution Level 3: High-risk Frequency Percentage 95% C.I. Frequency Percentage 95% C.I. Frequency Percentage 95% C.I. OSAS 78 74.3 65.08 Â– 81.81 8 7.6 3.86 Â– 14.41 16 15.2 9.50 Â– 23.42 PLMD 73 69.5 59.94 Â– 77.66 13 12.4 10.48 Â– 20.23 15 14.3 8.78 Â– 22.44 EDS 82 78.1 69.08 Â– 85.03 12 11.4 6.59 Â– 19.02 8 7.6 3.86 Â– 14.41 DSPS 75 71.4 61.98 Â– 79.27 8 7.6 3.86 Â– 14.41 19 18.1 11.83 Â– 26.68 SDI 71 67.6 58.02 Â– 75.90 13 12.4 7.34 Â– 20.19 18 17.1 11.02 Â– 25.57 Table 6 SDIS-C Subscale Percentages Overall n Percentage 95% C.I. Normal 59 57.84 48.14 Â– 66.97 Caution 11 10.78 6.13 Â– 18.28 High-risk 32 31.38 23.19 Â– 40.92 Table 7 Externalizing Problems Means and St andard Deviations by Sleep Score Sleep Category N Mean Standard Deviation Normal 71 58.55 11.76 Caution 13 63.31 8.80 High-risk 18 72.72 16.03
60 Table 8 Internalizing Problems Means and Standard Deviations by Sleep Score Sleep Category N Mean Standard Deviation Normal 71 57.01 10.46 Caution 13 59.15 6.59 High-risk 18 68.22 13.57 In order to determine that the ANOVA wa s the best test to use, the three assumptions of independence, normality, and homogeneity were cons idered (see Figure 3). The same data collection procedures and participants ensured that the assumption of independence was passed for the same reas ons that this assumption was passed for externalizing problems. Scores on the inte rnalizing subscale of the CBCL had a small positive skew (0.078), and the distribution was neither leptokurtic or platykurtic (-.015). Standard deviations of each condition revealed that the high-risk sleep disorder condition had the most variability of scores (S D = 13.57), while the caution sleep disorder condition had the least variability (SD = 6.59). After checking all assumptions, an ANOVA was conducted to determine whether or not a difference existed between scores on the internalizing pr oblems subscale of the CBCL, based on the category of sleep disorder. The level of overall sleep disorder risk was used as the categorical variable, wh ile the score on the internalizing problems subscale of the CBCL served as the continuous variable. Results of the ANOVA revealed that a significant difference existed between the three groups (F(2,99)=7.893, p =.001). This indicated that at the .05 level, there wa s a significant difference in parent-reported internalizing problems based on the overall level of sleep disorder.
61 A Tukey test was conducted in order to determine for which levels of overall sleep disorders risk there was a difference in internalizing problems. The Tukey test showed a difference between the normal and hi gh-risk levels of slee p disorders at a .05 confidence level. This indicates that childre n with high-risk factors for sleep disorders had significantly more internalizing problem s, as compared to children with no risk factors for sleep disorders. The differen ce between sample means was 11.21, with a 95% simultaneous confidence interval indicati ng that the difference between populations means was between 4.50 and 17.92. No differences were found between the caution level and either of the other 2 levels of sleep diso rders. Of the participan ts who were at high risk for a sleep disorder, 61% of these partic ipants had CBCL internalizing scores falling in the clinical range, while 16% had CBCL internalizing scor es in the borderline range and 22% had CBCL internalizing scores in th e normal range. Of the participants who were at normal or no risk for a sleep di sorder, 60% of these participants had CBCL internalizing scores falling in the norm al range, while 30% had CBCL internalizing scores in the borderline range and 10% had CBCL internalizing scores in the clinical range. The ANOVA and Tukey Test results indicate that chil dren who were rated as high-risk for a sleep disorder received si gnificantly higher scores on the internalizing problems subscale of the CBCL as compared to children who scored in the normal sleep range. Adaptive behavior functioning and sleep disorders The fourth research question sought to examine the relationship between normal, cautionary and high-risk range sleep disorder symptoms, as measured by the SDIS-C, and functional adaptive skills as measured by the ABAS-II. The distribution of adaptive
62 behavior functioning for each of the 3 groups can be seen in Figure 3. On the SDIS-C, Tscores of 60 and under are in the normal ra nge, 61 Â– 64 are in the caution range, and 65 and above are in the high-risk range. On th e ABAS-II, standard scores between 90 Â– 109 are in the average range, scores between 80 Â– 89 are in the low average range, scores between 70 Â– 79 are in the borderline range, and scores of 69 and below are in the extremely low range. The means and standard deviations for each sleep disorders risk classification on the ABAS-II, a measure of adaptive behavior functioning, are found in Table 9. 0.00.51.01.52.0SDIndex 40 60 80 100 120A B A S T o t a l The assumptions of independence, norma lity, and homogeneity were again tested to make certain that an ANOVA was a valid test to use. Scores on the ABAS-II had a small positive skew (0.233), and the distri bution was slightly platykurtic (-.735). Figure 3. Distribution of adaptive behavior sc ores by sleep disorder risk level.
63 Table 9 Adaptive Behavior Functioning Means and St andard Deviations by Sleep Score Sleep Category N Mean Standard Deviation Normal 71 76.66 19.28 Caution 13 71.31 19.15 High-risk 18 76.33 19.70 An ANOVA was conducted in order to de termine whether or not a difference existed between scores on the adaptive be havior functioning base d on the category of sleep disorder. Again, the level of overall sleep disorder risk was used as the categorical variable, while the score on the ABAS-II served as the continuous variable. The results revealed that the ANOVA was not significant (F(2,99)=0.426, p =.0.654), meaning that there were no significant differences in sc ores between childrenÂ’s adaptive behavior functioning based on the overall level of sleep disorder. Relationship between SDIS-C and CBCL sleep score To address the final research questio n, a Pearson Product Moment Correlation was conducted to evaluate whether or not a re lationship existed between the sleep scores measured by the CBCL and the Sleep Distur bance Index (SDI) score measured by the SDIS-C. The sleep score on the CBCL is calculated by the responses to 6 items, whereas the SDI score on the SDIS-C is calculated by the responses to 41 items. The results revealed that there was a moderate correlation (r = 0.583) (Franzblau, 1958 ), which was significant at the 0.01 level us ing a 2-tailed test. The rela tionship between the sleep scores as measured by the SDIS-C and the CBCL can be seen in Figure 4.
64 405060708090SDIndex_T_Score 50 60 70 80 90 100S l e e p T S c o r e Summary The findings in the present study indicat e that a significant number of young children may be at risk for at least one type of sleep disorder. In this sample, 31% of children were found to be at high -risk for at least one type of sleep disorder while 10% of children were found to be at cautionary-risk fo r at least one sleep di sorder. Overall, 41% of the children in this sample were reported by their parents to be experiencing significant sleep problems. Internalizing and externalizing behavior problems in young children were also associated with being at risk for symptoms related to a sleep disorder. Young children who were reported to be at high-risk for a sleep disorder were more likely than young Figure 4. Scatterplot of sleep scores as m easured by the CBCL sleep score and the SDIS-C sleep index score.
65 children who were reported to be at normal risk for sleep disorders to exhibit significant internalizing and externalizi ng problematic behaviors. In other words, the young children who were reported as having poor er sleep quality were also reported as displaying higher rates of challenging behaviors. Despite th ese significant results, no relationship was found between young childrenÂ’s adaptive functioni ng and their reported risk for having a sleep disorder. However, the scores on the ABAS-II did reveal that this sample showed signs of developmental delays. Based on these results, the entire sample of children was considered high risk and this could acc ount for the spurious relationship between adaptive functioning and sleep disorder risk. This study also found a moderate relationship between the overall sleep index score as meas ured by the SDIS-C and the sleep score on the CBCL, indicating that the two scores measure similar constructs.
66 Chapter 5 Discussion The International Sleep Task Force Co mmittee (2004) estimated that 20-to-25% of all children experience sleep problems in childhood. An estimated 15% of all children may have a significant sleep disorder that is negatively impacting their academics, behaviors, social-emotional development, h ealth, and/or safety (Brown & DuPaul, 1999; Kubisyn, 1999). Early intervention programs can contribute towards the reduction of risks associated with children who have pedi atric sleep disorders. Given the link between academic underachievement, problematic be havior, and sleep problems (Gozal, 1998), early identification and treatment of sleep dist urbances may serve to reduce the risk of negative outcomes in later life. The primary purpose of this study was to assess the prevalence rates of children ages 24 months to 5 years refe rred to a clinic setting displa ying sleep disorder symptoms, and to analyze the relationship between sl eep disorder symptoms and internalizing, externalizing, and adaptive behaviors. A sec ondary goal of this study was to assess the relationship between sleep problems as meas ured by the CBCL and the sleep index score as measured by the SDIS-C. This final ch apter addresses each re search question and discusses the implications of the research findings on the practice of school psychology. Additionally, this chapter discusses the limitati ons of this study and explores areas for future research in pediatric sleep disorders.
67 Research Question 1 What is the prevalence rate of children at ri sk for sleep disorders, as measured by the Sleep Disorders Inventory for Students Child renÂ’s version (SDIS-C), in children ages 2 Â– 5 years presenting to a university-based child development clinic for assistance? While previous research has evaluated the prevalence of bedtime resistance behaviors in preschool age children (Johnson, 1991; Kerr & Jowett, 1994; Mindell, Owens, & Carskadon, 1999; Owens, Spirito, Mc -Guinn, & Nobile, 2000 ), very little research has been conducted on the prevalence rates of sleep disorders in this population. Three recent studies have evaluated the preval ence rates of sleep problems in pediatric populations using the SDIS-C. Luginbuehl ( 2004) screened 595 students from across school and clinical settings for sleep diso rders and found that approximately 20% of participants were at high-risk for at least one sleep diso rder. Ax (2006), who evaluated prevalence rates of sleep disorder risk in 216 secondand third-gr ade general education students, also found that 20% of participants were at signific ant risk for sleep disorders. Witte (2006) found that 32% of an at risk pres chool sample (n = 86) was at high-risk for at least one type of sleep di sorder and 10% of the sample was at cautionary risk for at least one type of sleep disorder. Findings in this study revealed that 31% of the preschool age children in this sample were at high-risk for at least one sleep disorder and 10% of young children were at cautionary risk for at leas t one sleep disorder. In other words, one out of every three children in this study as well as the Witte ( 2006) study was at high-risk for having a sleep disorder. These rates are alarming; especially since so many children with sleep disorders go undiagnosed (National Sleep Disorder Research Plan, 2005). Furthermore, because so
68 many children with sleep disorders go undiagno sed, the prevalence rate s of specific sleep disorders (e.g., OSAS, RLS, PLMD, D SPS) in young children are still unknown. This study found that 18% of the sample was at high-risk fo r DSPS. It is not surprising that 18% of this sample was at hi gh-risk for DSPS, which is also referred to Behavioral Insomnia of Childhood (BIC) fo r preschool age children. BIC is most typically caused by a lack of an enforced bedtime or poor sleep hygiene. Because this sample was an at-risk populat ion, and their families were bringing their children to a medical clinic because of developmental or behavioral concerns, it is very likely that these caregivers have problems setting limits (e.g., structured bedtimes) with these children. Research conducted by Owens, Spir ito, Mc-Guinn, & Nobile (2000), which did not use norm-referenced measures, found th at 25 Â– 50% of preschool age children exhibited bedtime resistance behavior. Witte (2006), who did use a structured, normreferenced sleep scale to ev aluate prevalence rates found th at 20% of her preschool age sample was at high-risk for DSPS, which is consistent with fi ndings from this study. Previous research has also found that th e rate of OSAS in preschool age children is between 1-3% (Ali, Pitson, & Stradling, 1993; Gisalson & Benediktsdottir, 1995). This study found that 15% of the sample was at high-risk for OSAS. Witte (2006) found that 11.6% of the sample was at high-risk for OS AS. The samples from both studies evaluated at risk populations. This study was made up of children coming to a medical clinic because of health or behavioral concerns, so it is possible that these children have higher rates of sleep problems than a general pr eschool population. Additionally, tonsils and adenoids are at their largest among the enti re population between the ages of 3 and 6
69 years, and this coincides w ith the peak incidence of ch ildhood OSAS (Jeans, Fernando, Maw, & Leighton, 1981). The prevalence rates of preschool age children diagnosed with PLMD are still unknown (McLaughlin-Crabtree, Ivanenko, OÂ’Brien, & Gozal, 2003; Owens, 2005; Picchietti & Walters, 1999). Previous research has found that approximately 8% of their sample ages 5 Â– 7 years was diagnosed w ith PLMD via polysomnography (McLaughlinCrabtree, Ivanenko, OÂ’Brien, & Gozal, 2003). Th is study found that 14% of the sample was at high-risk for PLMD. The rates in this study may differ from previous research because the former study actually diagnosed PLMD in children, while this study used a screening tool to assess risk for PLMD. The prevalence of EDS also was assessed in this study. Only 7% of the sample was at high-risk for EDS. This finding is so mewhat surprising give n the larger numbers of young children found to be at high-risk for ot her sleep disorders. Sp ecifically, if large numbers of children are displayi ng symptoms of sleep disorders, it could be assumed that their sleep quality is poor, and this in tu rn would impact their daytime performance. However, this study found that a smaller num ber of children were experiencing daytime sleepiness compared to the number of children rated by their parents as being at high risk for PLMD, OSAS, or DSPS. While prevalen ce of EDS was not assessed in previous research, the relationship between EDS and ex ternalizing behaviors has been evaluated (Chervin, Dillon, Archbold, & Ruzicka, 2003; Lavigne et al., 1999; Owens, Opipari, Nobile, & Spirito, 1998). It is possible that parents misinterpret daytime sleepiness in toddlers since it is often manifested in young children by behaviors such as increased activity, aggression, impulsivity, acting out beha vior, poor concentration, and inattention
70 (Carskadon, Pueschel, & Millman, 1993; Gu illeminault et al., 1982). Parents do not usually associate such behaviors with sleepine ss, due in part to the lack of information available and disseminated to parent s about pediatric sleep problems. A major reason only 1-2% of children with sleep disorders are properly diagnosed (National Center of Sleep Disorders Research, 2003) is because there is limited research on pediatric sleep disorders. Pediatric sleep research with preschool populations only accounts for 3% of all sleep research be ing conducted (Chase, Lydic, & OÂ’Connor, 1991). The majority of the pediatric sleep research that does evaluate preschool populations focuses on bedtime resistance (B eltramini & Herzog, 1983; Crowell, Keener, Ginsburg, & Anders, 1987; Johnson, 1991; Mindell et al, 1994). This study is unique in that the prevalence rates of common sleep disorders in children were evaluated in children ages 2 to 5 years. Research evaluating the prevalence rates of common sleep diso rders in pediatric samples is starting to emerge (Ax, 2006; Luginbuehl, 2004; Witte, 2006). This study, as well as the research conducted by Ax (2006) and Witte (2006), found that the most common sleep problem among pediatric populat ions was DSPS. OSAS had the second highest prevalence rate across the three stud ies. The prevalence rates for all sleep disorders in this study and the Witte (2006) study were extremely similar, while the prevalence rates for sleep disorders found in Ax (2006) were significantly lower. One possible reason for the discrepancy in rates is because Ax (2006) assessed children from the general population, while this study and Witte (2006) evaluated children from at risk populations. Additionally, the sample from this study and Witte (2006) evaluated
71 preschool age children, while Ax (2006) used second and third grade students, which could also account for the diffe rences in prevalence rates. It is important for school psychologists to be knowledgeable of pediatric sleep disorders and prevalence rates in order to he lp ameliorate problems associated with these disorders. If sleep disorders in young children are identified and treated at the earliest possible age, the negative academic, behavi oral, emotional, and health outcomes associated with sleep problems can potenti ally be prevented. Sc hool psychologists can assist in the early identification of sleep disorders in young child ren through facilitating universal screenings. Additionally, school psychologists can educate others on the importance of good sleep hygiene and the negative impact that poor sleep can produce. Research Question 2 What is the relationship between normal, c autionary and high-risk range sleep disorder symptoms, as measured by the SDIS-C, and externalizing behavior problems, as measured by the Child Behavior Checklist (CBCL)? Previous research has found that sleep problems in young children are associated with behavioral problems, such as hyperactivity, aggres sion, impulsivity, acting out behavior, poor concentrati on, and inattention (Carska don, Pueschel, & Millman, 1993; Broughton & Shimizo, 1995; Fallone, Ownens, & Deane, 2002; Owens, Opipari, Nobile, & Spirito, 1998). Preschool age children with sleep problems have shown high levels of tantrums and other disruptive behaviors (Owens-Stively et al., 1997; Zuckerman, Stevenson, & Bailey, 1987). The findings in this study are consistent with previous research. Specifically, children who were at hi gh-risk for sleep disorders had significantly higher rates of externalizing be haviors compared to children who were at normal-risk for
72 sleep problems. There were not, however, si gnificant discrepanc ies observed between children with cautionary risk and children wi th normal sleep meaning that externalizing behavioral problems were reported mostly in high-risk group only. Id entical results were found in two recent studies (Ax, 2006; Witte, 2006) A number of previous studies have suggested that the symptoms of Attenti on-Deficit/Hyperactivity Disorder (ADHD) are overrepresented in children diagnosed with va rious sleep disorders, especially PLMD (Chervin & Archibold, 2001; Guilleminault et al. 1981; Picchietti & Walters, 1999). While this study did not assess specific extern alizing behaviors, such as rule-breaking behavior or aggression, it did identify childre n reported with higher risk of having a sleep disorder as also exhibiting mo re externalizing behaviors. The majority of previous research in th e area of pediatric sleep medicine used either broader age ranges or older children. Th is study evaluated childr en ages 2-5 years. Although there are studies that evaluated the relationship between ex ternalizing behaviors and sleep problems in preschool populations informal sleep measures (e.g., asking parents to report the usual time their child fe ll asleep and woke up, the number of naps taken per week the average length of naps, etc. ) were used. This study as well as research conducted by Witte (2006) used a norm-referenced sleep-screening instrument to assess sleep disorder risk in young children. It is important for school psychologi sts to recognize the high comorbidity between sleep problems and externalizing behaviors. School ps ychologists commonly receive referrals for problematic behavior (Kratochwill, Kratochwill, Albers & Shernoff, 2004). Symptoms such as disorganization, im pulsivity, poor planning, poor attention, and hyperactivity are commonly misdiagnosed as behavioral dysregulation disorders.
73 However, these symptoms are also seen in children with sleep disorders. School psychologists who are knowledgeable of sleep di sorder symptoms and the impact of sleep on daily functioning can develop sleep-relate d hypotheses and use assessment tools to confirm or disconfirm hypotheses, which may a ssist in the problem solving process. Research Question 3 What is the relationship between normal, c autionary and high-risk range sleep disorder symptoms, as measured by the SDIS-C, and internalizing behavior problems, as measured by the Child Behavior Checklist (CBCL)? The majority of research examining th e association between sleep problems and internalizing problems has focused on adults (Gregory et al., 2005) and has identified a positive relationship between sleep disorders and internalizing problems (Fallone, Acebo, Seifer, & Carskadon, 2005). Gregory and OÂ’ Connor (2002) found that sleep problems were moderately but significantly correlated with anxiety/depression in a sample of 4 Â– 15 year olds. A longitudinal study conducted by Gregory et al. (2005) found that children with persistent sleep problems have more internalizing problems than children without persistent sleep problems. The same study also found that 46% of the sample that reported significant sleep problems at 9 ye ars old had anxiety in adulthood. Another study conducted by Paavonen et al. (2002) found th at 8 and 9 year old children with more severe sleep problems were more likely to have emotional problems than children without sleep problems. While previous st udies have found some relationship between sleep problems and internalizing problems in childhood, these studies used older samples of children and informal measures of sleep. The present study contributed to the empirical literature by examining the
74 relationship between parent-re ported sleep disorder risk and internalizing behavior problems in preschool age child ren using standardized measur es. Findings revealed that children who were reported to be at high-risk for sleep disorders had significantly higher rates of internalizing behaviors compared to children who were repor tedly at normal-risk for sleep problems. No significant discre pancies were observed between children reported to be at cautionary risk and childre n with reportedly normal sleep meaning that internalizing behavioral problems were seen mostly in the group of children reported to be high-risk for a sleep disorder. Similar re sults were found in recent studies (Ax, 2006; Witte, 2006). Although these findings are consistent with previous literature on older populations, the significant findings in this study are somewhat surprising given the fact young children with internalizing problems tend to exhibit more externalizing behaviors than internalizing behaviors (Mash & Ba rkley, 2003). In preschool age children, irritability, uncooperativeness, apathy, and disinterest are more common than having a depressed mood (Kashani, Holcomb & Orva schel, 1986). Similarly, preschoolers are unlikely to report feelings associated with internalizing disord ers (Ryan et al., 1987). Less than 3% of preschool-age children have diagnosed internalizi ng disorders (Costello & Angold, 1995). Because internalizing pr oblems are not commonly reported in preschool age children (with th e exception of Separation A nxiety), it was unexpected to see the significant differences in internalizi ng behavior scores between children at highrisk for sleep disorders and ch ildren at normal risk for sleep disorders found in this study. This study differed from other studies eval uating sleep and inte rnalizing behavior problems in a number of ways. Previous studies assessed sleep problems through measures using a limited number of questions and used older children. This study as well
75 as research conducted by Witte (2006) used a norm-referenced sleep-screening instrument to assess sleep disorder risk in young children. Additionally, this study, Witte (2006), and Ax (2006) distinguished internal izing behavior problems by sleep disorder risk, whereas the previous studies merely examined the correlation between sleep disturbance and internalizing behavioral symptoms. School psychologists should be cognizan t of research de monstrating that internalizing behavior problems have been found to be more significant in children reported to be at risk for sleep disorders. Through early identi fication of both sleep disorder risk as well as risk for internalizing behavior pr oblems, school psychologists can aid in prevention and early intervention strategies for these children. Specifically, because both sleep problems and internalizing behavior problems are associated with future academic impairments, school psychologists can help to prevent these problems. Research Question 4 What is the relationship between children at risk for sleep disorders in the normal, cautionary, and high-risk r ange, as measured by the SDIS-C and their functional adaptive skills as measured by the Adap tive Behavior Assessment System Â–Second Edition (ABAS-II)? Adaptive behavior is oftentimes evalua ted in preschool and special education programs for determining need, program pl anning, and assessing performance outcomes (Harrison, 1990; Harrison & Boney, 2002; Reschly, 1990). Children who experience difficulty with adaptive behaviors are at in creased risk for experiencing problems with the adjustment to school, which in turn l eads to difficulties with social and academic competence (Blair & Peters, 2003). Previous studies have found a relationship between
76 poor adaptive skills and externalizing and in ternalizing problems (P alermo et al., 2002), but there have been no empirical studies ev aluating the relationship between sleep and adaptive behavior. Since there is emerging evidence linking sleep problems to internalizing and externa lizing behaviors (Witte, 2006), it was hypothesized that the present study would find a si gnificant relationship between poor adaptive skills and reported risk for a sleep disorder. The results of this study did not support this hypothesis. No differences in adaptive behavior were observed between young children at high, cautionary, or normal risk for sleep diso rders. Further examination of the data revealed that the sample had poor adaptive skil ls overall, with a mean score falling in an extremely low range for adaptive behavior. In fact, the high-risk and normal risk sleep disorder groups had the same mean adaptiv e behavior score and the same standard deviation (standard score = 76; standard deviation = 19), while the cautionary group mean score was only 5 points lower and the stan dard deviation was the same as the other two groups (standard score = 71; standard deviation = 19). One possible reason the finding of no difference in adapti ve behavior between the categor ies of sleep disorder risk is that the children in the sample were coming to the medical clinic because of existing developmental or behavioral concerns. Bo th developmental delays and behavioral problems can be assessed through adaptive sk ill measures. Typically, children with developmental delays and behavioral problem s exhibit poor adaptive skills and this was the case with this sample. Future research should evaluate the relationship between adaptive skills and sleep disord er risk in the general popula tion where there may be more variability among adaptive behavioral functioning.
77 School psychologists must be knowledgeable of the impact of adaptive behavior on school functioning and how to measure and improve adaptive func tioning in children. School programs and early intervention services are typically the systems that identify children as having problems w ith adaptive functioning. Wit hout early identification of adaptive skill problems, the likelihood that e ffective interventions will be implemented is slim. Therefore, it is important that school psychologists identify children with adaptive skill problems early, and then help the interdisciplinary problem solving team design and implement interventions to increase adaptive skill development. Research Question 5 What is the relationship between the sleep pr oblem score on the CBCL and the total sleep index score on the SDIS-C? The SDIS-C is a screening tool de signed to assist sc hool and clinical psychologists to measure childrenÂ’s risk for ha ving sleep disorders that interfere with academic and behavioral success. The sleep di sorders that are assessed in preschool age children include: OSAS, EDS, PLMD, and DS PS. There are specifi c items on the SDISC that ask about symptoms of each of th e aforementioned sleep disorders. The CBCL was developed to assess internalizing and externalizing behaviors in children. On the childrenÂ’s version of the CBCL, sleep probl ems are assessed through six specific items (e.g., doesnÂ’t want to sleep alone, has trouble ge tting to sleep, nightmares, resists going to bed at night, sleep less than most kids during day and/or night, wake s up often at night). The results of the present study found a moderate yet significant correlation between the SDIS-C and the CBCL. This m oderate relationship indicates that both measures assess similar constructs, but cannot be used interchangeably. This finding was
78 expected given that the CBCL measures sleep problems as a general construct, while the SDIS-C differentiates between specific sleep disorders in addition to providing an overall sleep disturbance score. The CBCL cannot be used to assess sleep disorder risk, nor does it distinguish various ty pes of sleep problems. School psychologists should be using both a measure of sleep disorder risk as well as a measure of risk for internalizing and externalizing problems when engaging in the problem-solving process. As mentioned pr eviously, a large number of children with sleep disorders go either undiagnosed or misd iagnosed. This problem could potentially be ameliorated if school psychologists promoted universal screenings to aid in the early identification of children with a variety of pr oblems, including sleep disorders. It is only through early identification th at early interventions can be implemented. If sleep disorders and/or psychosocial disorders are left untreated, a number of deleterious outcomes could result, incl uding academic failure. Implications for school psychologis ts: School-based health services A major goal of school psychology is to support the academic achievement and mental health of students. For this reason, sc hool psychologists are exp ected to assist in the improvement of instructional outcomes by assessing studentÂ’s barr iers to learning. One major barrier to learning is chro nic health problems, including sleep problems/disorders. A number of reforms in education as well as in public health have documented the importance of addressing the chronic health needs of children in schools (Power, Shapiro, & DuPaul, 2003). The Preventive Health Amendments of 1992 (PL 102-531), mandated the coordination between the he alth care and educational sy stems in training educators
79 about the risks associated with medical conditions, such as sleep disorders. The importance of health promotion in schools also was outlined in The Goals 2000: Educate America Act (1994), which specified that hea lth-related problems ha ve negative impacts on school performance. More recently, the Indi viduals with Disabili ties Education Act (IDEA) Â– Amendments of 2004 (IDEA, 2004) specifi ed that children with disabilities are entitled to have their educatio nal and health needs addresse d in schools. This emphasis on providing health-based support in schools has expanded the mission for schools to include the promotion of health for all student s and the removal of barriers to learning for children with health disorders (Kol be, Collins, & Cortese, 1997). As a result of legislation, school psychol ogists will be increasingly placed in new roles of coordinating educational, health, and mental health services for children with medical conditions (Power & Blom-Hoffman, 2004). School psychologi sts have expertise in school ecology and community-s chool relations, in addressing childrenÂ’s mental health problems, and emerging skills in addressing chil drenÂ’s health problems. Because of these knowledge and skills, school psychologists can se rve as evaluators and interventionists as well as take a greater role in indirect school-based servic es through serving as systems consultants, program developers, and program evaluators within school-based health centers (Bradley-Johnson & Dean, 2000; Powe r & Blom-Hoffman, 2004; Reeder et al., 1997; Tharinger, 1995). In order to successf ully serve in this newer role, school psychologists need training in pediatric health issues and the educational implications of children with health disorders, including sleep disorders (Power, DuPaul, Shapiro, & Parrish, 1995). Furthermore, sc hools need to establish schoo l-based health and mentalhealth programs to promote academic success through the removal of these barriers to
80 instruction (Adelman, 1996; Power, 2000). School psychologists who are knowledgeab le about pediatric h ealth conditions can serve as a liaison between the school system and the medical system (Power & BlomHoffman, 2004). For instance, if a school and medical system or larg e pediatric practice established a partnership, school psychologists would be able to assist in making healthrelated referrals, especially since school psychologists are in a unique position to access the majority of children. Likewise, the sc hool psychologist can assist the medical professional in data collecti on at the school to aid in th e progress monitoring process. Through this collaborative system, schools a nd medical systems can impact students on a universal level of health promotion (Kol be, Collins, & Cortese, 1997; Short & Talley, 1997). In an ideal system, school psychologists would facilitate the administration of universal screenings including measures of sleep disorder risk. Through universal screenings, early identificat ion of sleep problems can o ccur. Concurrently, school psychologists can educate school personnel as well as parents and children on various health disorders, such as sleep disorders, and their impact on school performance. When school psychologists are skilled and experien ced in addressing the health needs of children, they can have a pivotal role in provi ding relevant health-r elated information to parents, children, and school personnel (P ower & Blom-Hoffman, 2004). Educational programs on sleep have shown to be associated with better sleep hygiene as well as sleep quality (Brown, Buboltz, & S oper, 2006; Simore, Crassar d, Rechatin, & Locard, 1987). Educating others on the importance of sleep quality as well as the importance of sleep screening increases the treatme nt acceptability of univers al screenings (Joshi, 2004;
81 Kazdin, 1981). Once all students have been screened, school psychologists as well as other school personnel can identify which st udents need further evaluations and/or support. If a child is found to be at risk for a sleep disorder, school psychologists who work in a school with a medical partnershi p can recommend that the medical practice refer the child to a sleep specia list for further evaluation given that parental permission is granted. School psychologists can also e ducate parents on the child criteria for diagnosing sleep disorders (Rosen, DÂ’A ndrea, & Haddad, 1993). With this knowledge, parents will know what to look for and know wh at questions to ask of physicians, which can increase the likelihood that their child receives a proper dia gnosis. Through use of a problem-solving process, an intervention plan for a child with a sleep problem/disorder can be designed by an interdisciplinary team including the parents, school psychologist, physicians involved in the childÂ’s treatment, as well as any school personnel that could aid in intervention development and impl ementation. Next, school-based and medical interventions can be implemented with the sc hool psychologist servi ng in a consultative role. Finally, the school psychologist can ai d in monitoring the childÂ’s response to intervention to maximize educat ional and health outcomes. Limitations and implications for future research This study evaluated the prevalence rates a nd behaviors related to sleep disorders in a sample of 104 children ages 2-5 years. Studies that typically assess prevalence rates tend to have much larger sample sizes. Becau se of the small sample size, there is an increased chance of error in estimating the prevalence rates in the population. This study also only sampled children attending a medical clinic for developmental or behavioral concerns. Typically children with developmenta l and behavioral problems have increased
82 sleep problems as well as increased problems in daily functioning. Because of this unique sample, the prevalence rates only represen t at-risk preschool popul ations; the results cannot be generalized to gene ral preschool populations. The overall low adaptive scores could also have reflected the at-risk nature of this sample. This study also analyzed prevalence rates by an age-group (e.g., presc hool). As a result, no conclusions can be drawn on how the prevalence rates may differ by ag e. Future research should evaluate the prevalence rates of preschool children in the general population and should use a large sampling for more representative prevalence rates. Another limitation of this study is the possible threat of reactivity. Reactivity is when people alter their responses because th ey are aware that their responses will be analyzed (Johnson & Christensen, 2004). Pare nts who completed the measures wanted help for their children. It is possible that parents could have over-reported certain behaviors and under-reported others in order to receive medical attention. Rater bias could have also impacted parental reporti ng. For instance, some parents have extremely low tolerance for specific behaviors and then will rate such behaviors much more severely than parents who are more tolerant of specific behaviors. Future research can assess these threats to validity through adding measures that assess parental expectations as well as parenting stress. Teacher/Caregiver versions of the behavi or ratings scales can also be administered in future research so th at behavior across sett ings can be assessed. Future research should also sample child ren from the general population therefore obtaining a more representative sample of the general preschool population. In this study, the parents of the sample completed the measures at one point in time. As part of the medical clinicÂ’s protocol, if a child is reported to be high-risk for a
83 sleep disorder, then a recommendation is written to the childÂ’s pediatrician that the child be referred to a pediatric sleep specialist. While the clinic does follow-up with the families in regard to whether or not sleep pr oblems were diagnosed, this information was not recorded and therefore not analyzed. Future studies can ev aluate the accuracy of sleep disorder screening tools in identifying child ren who do have diagnosable sleep disorders. Future studies also can use sleep screeni ng, externalizing, intern alizing, and adaptive behavior measures after interventions have b een implemented to assess the effectiveness of the intervention. This study evaluated the relationship betw een overall sleep disorder risk and internalizing, externalizing, and adaptive beha viors as general constructs, rather than evaluating sleep in relation to the specific behaviors that make up each construct (e.g., aggression, inattention, depressed affect for behavior). Similarl y, sleep disorder risk total score was used to classify children into high, cautionary, and norma l risk groups for the purpose of comparing those groupsÂ’ behaviors. Future research can compare children at high-risk for specific sleep di sorders to children in the cautionary and normal groups to determine if group differences exist in specifi c internalizing, extern alizing, or adaptive behaviors. Conclusion The current study evaluated the prevalen ce rates of common sl eep disorders in children ages 2 Â– 5 years and evaluated the re lationship between sleep disorder risk and behavioral outcomes. Thirty-one percent of young children in the sample were reported as being at high-risk for at le ast one type of sleep disorder DSPS or behavioral insomnia of childhood was the most common sleep disord er that the young children in this sample
84 were reported as being at high-risk for, followed by OSAS, and then PLMD. These findings indicate that preschool age children maybe at risk for sleep disorders, and that screening for sleep disorders is important. Young children who were reported as high-risk for a sleep disorder displayed higher levels of both internalizi ng and externalizing behaviors. No differences in sleep disorder risk levels were found in adaptive behavioral functioning. School psychologists can play a pivota l role in helping to identify children at risk for sleep disorders through universal scr eenings and implementa tion of interventions. Additionally, school psychologists can educate parents, teachers, and children on the importance of good sleep hygiene as well as the signs and symptoms of sleep disorders. Finally, school psychologists can promote syst em change to incorporate health-based services in schools, thus promoting benefici al health and educational outcomes for all students.
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